Recommendations

The Recommendations API identifies consumption patterns from your transaction information in order to provide recommendations. These recommendations can help your customers more easily discover items that they may be interested in.
By showing your customers products that they are more likely to be interested in, you will, in turn, increase your sales.

Get build by id

Retrieves information about the build, including parameters used to build it.

Open API testing console

Request URL

Request parameters

string

Unique identifier of the model

integer

Format - int64. Unique identifier of the build

Request headers

string
Subscription key which provides access to this API. Found in your Cognitive Services accounts.

Request body

Response 200

OK

{
  "id": 0,
  "description": "string",
  "type": "string",
  "modelName": "string",
  "modelId": "string",
  "status": "string",
  "statusMessage": "string",
  "startDateTime": "string",
  "endDateTime": "string",
  "modifiedDateTime": "string",
  "buildParameters": {
    "ranking": {
      "numberOfModelIterations": 0,
      "numberOfModelDimensions": 0,
      "itemCutOffLowerBound": 0,
      "itemCutOffUpperBound": 0,
      "userCutOffLowerBound": 0,
      "userCutOffUpperBound": 0
    },
    "recommendation": {
      "numberOfModelIterations": 0,
      "numberOfModelDimensions": 0,
      "itemCutOffLowerBound": 0,
      "itemCutOffUpperBound": 0,
      "userCutOffLowerBound": 0,
      "userCutOffUpperBound": 0,
      "enableModelingInsights": true,
      "splitterStrategy": "string",
      "randomSplitterParameters": {
        "testPercent": 0,
        "randomSeed": 0
      },
      "dateSplitterParameters": {
        "splitDate": "string"
      },
      "popularItemBenchmarkWindow": 0,
      "useFeaturesInModel": true,
      "modelingFeatureList": "string",
      "allowColdItemPlacement": true,
      "enableFeatureCorrelation": true,
      "reasoningFeatureList": "string",
      "enableU2I": true
    },
    "fbt": {
      "supportThreshold": 0,
      "maxItemSetSize": 0,
      "minimalScore": 0.0,
      "similarityFunction": "string",
      "enableModelingInsights": true,
      "splitterStrategy": "string",
      "randomSplitterParameters": {
        "testPercent": 0,
        "randomSeed": 0
      },
      "dateSplitterParameters": {
        "splitDate": "string"
      },
      "popularItemBenchmarkWindow": 0
    },
    "sar": {
      "supportThreshold": 0,
      "cooccurrenceUnit": "User",
      "similarityFunction": "Jaccard",
      "enableColdItemPlacement": true,
      "enableColdToColdRecommendations": true,
      "enableModelingInsights": true,
      "enableU2I": true,
      "splitterStrategy": "RandomSplitter",
      "randomSplitterParameters": {
        "testPercent": 0,
        "randomSeed": 0
      },
      "dateSplitterParameters": {
        "splitDate": "string"
      },
      "popularItemBenchmarkWindow": 0,
      "enableUserAffinity": true,
      "allowSeedItemsInRecommendations": true,
      "enableBackfilling": true
    }
  }
}
{
  "type": "object",
  "properties": {
    "id": {
      "format": "int64",
      "description": "Unique build identifier",
      "type": "integer"
    },
    "description": {
      "description": "Description provided by user (BuildRequestInfo.Description)",
      "type": "string"
    },
    "type": {
      "description": "Type of build: Recommendation - 1, Ranking - 2, Fbt - 3",
      "type": "string"
    },
    "modelName": {
      "description": "Name of the Model associated this build",
      "type": "string"
    },
    "modelId": {
      "description": "ID of the Model associated this build",
      "type": "string"
    },
    "status": {
      "description": "Status of the build: NotStarted, Running, Cancelling, Cancelled, Succeeded, Failed",
      "type": "string"
    },
    "statusMessage": {
      "description": "Details if available about build status",
      "type": "string"
    },
    "startDateTime": {
      "description": "Build start time",
      "type": "string"
    },
    "endDateTime": {
      "description": "Build end time",
      "type": "string"
    },
    "modifiedDateTime": {
      "description": "Last build modified time",
      "type": "string"
    },
    "buildParameters": {
      "type": "object",
      "properties": {
        "ranking": {
          "type": "object",
          "properties": {
            "numberOfModelIterations": {
              "format": "int32",
              "description": "The number of iterations the model performs. \r\n            The higher the number, the better accuracy, but compute time will be higher.",
              "type": "integer"
            },
            "numberOfModelDimensions": {
              "format": "int32",
              "description": "The number of dimensions relates to the number of 'features' the model will try to find within your data. \r\n            Increasing the number of dimensions will allow better fine-tuning of the results into smaller clusters. \r\n            However, too many dimensions will prevent the model from finding correlations between items.",
              "type": "integer"
            },
            "itemCutOffLowerBound": {
              "format": "int32",
              "description": "Defines the item lower bound for usage condenser.",
              "type": "integer"
            },
            "itemCutOffUpperBound": {
              "format": "int32",
              "description": "Defines the item upper bound for usage condenser.",
              "type": "integer"
            },
            "userCutOffLowerBound": {
              "format": "int32",
              "description": "Defines the user lower bound for usage condenser.",
              "type": "integer"
            },
            "userCutOffUpperBound": {
              "format": "int32",
              "description": "Defines the user upper bound for usage condenser.",
              "type": "integer"
            }
          },
          "description": "Build parameters for build of type \"Ranking\""
        },
        "recommendation": {
          "type": "object",
          "properties": {
            "numberOfModelIterations": {
              "format": "int32",
              "description": "The number of iterations the model performs. \r\n            The higher the number, the better accuracy, but compute time will be higher.",
              "type": "integer"
            },
            "numberOfModelDimensions": {
              "format": "int32",
              "description": "The number of dimensions relates to the number of 'features' the model will try to find within your data. \r\n            Increasing the number of dimensions will allow better fine-tuning of the results into smaller clusters. \r\n            However, too many dimensions will prevent the model from finding correlations between items.",
              "type": "integer"
            },
            "itemCutOffLowerBound": {
              "format": "int32",
              "description": "Defines the item lower bound for usage condenser.",
              "type": "integer"
            },
            "itemCutOffUpperBound": {
              "format": "int32",
              "description": "Defines the item upper bound for usage condenser.",
              "type": "integer"
            },
            "userCutOffLowerBound": {
              "format": "int32",
              "description": "Defines the user lower bound for usage condenser.",
              "type": "integer"
            },
            "userCutOffUpperBound": {
              "format": "int32",
              "description": "Defines the user upper bound for usage condenser.",
              "type": "integer"
            },
            "enableModelingInsights": {
              "description": "Enable or disable metrics computation for the model.",
              "type": "boolean"
            },
            "splitterStrategy": {
              "description": "Defines the splitter strategy to be used by the build.\r\n            RandomSplitter splits the usage data in train and test sets based on the given\r\n            randomSplitterParameters value.\r\n            LastEventSplitter splits the usage data in train and test sets based on the last\r\n            transaction for a each user.",
              "type": "string"
            },
            "randomSplitterParameters": {
              "type": "object",
              "properties": {
                "testPercent": {
                  "format": "int32",
                  "description": "The percentage of data from the usage file that will be put in the test set\r\n            during splitting.",
                  "type": "integer"
                },
                "randomSeed": {
                  "format": "int32",
                  "description": "Number used to calculate the startig value of random sequence based on which\r\n            test set data is selected.",
                  "type": "integer"
                }
              },
              "description": "Specifies the parameters to be used for random splitter."
            },
            "dateSplitterParameters": {
              "type": "object",
              "properties": {
                "splitDate": {
                  "format": "date-time",
                  "description": "The split date at which the usage file data is put in the test set\r\n            during splitting.",
                  "type": "string"
                }
              },
              "description": "Specifies the parameters to be used for date splitter."
            },
            "popularItemBenchmarkWindow": {
              "format": "int32",
              "description": "Specifies the parameters to be used for computing popular items for modeling insights. (in number of days)",
              "type": "integer"
            },
            "useFeaturesInModel": {
              "description": "Indicates if features can be used in order to enhance the recommendation model.",
              "type": "boolean"
            },
            "modelingFeatureList": {
              "description": "Comma-separated list of feature names to be used during build.",
              "type": "string"
            },
            "allowColdItemPlacement": {
              "description": "Indicates if the recommendation should also push cold items via feature similarity.",
              "type": "boolean"
            },
            "enableFeatureCorrelation": {
              "description": "Indicates if features can be used in reasoning.",
              "type": "boolean"
            },
            "reasoningFeatureList": {
              "description": "Comma-separated list of feature names to be used for reasoning sentences (e.g. recommendation explanations).",
              "type": "string"
            },
            "enableU2I": {
              "description": "Allow the personalized recommendation a.k.a. U2I (user to item recommendations).",
              "type": "boolean"
            }
          },
          "description": "Build parameters for build of type \"Recommendation\""
        },
        "fbt": {
          "type": "object",
          "properties": {
            "supportThreshold": {
              "format": "int32",
              "description": "Number of co-occurrences of items to be considered for modeling.",
              "type": "integer"
            },
            "maxItemSetSize": {
              "format": "int32",
              "description": "Bound for number of items in a frequent set.",
              "type": "integer"
            },
            "minimalScore": {
              "format": "double",
              "description": "Minimal score that a frequent set should have in order to be included in the returned results.",
              "type": "number"
            },
            "similarityFunction": {
              "description": "Defines the similarity function to be used by the build. \r\n            Lift favors serendipity, Co-occurrence favors predictability, and Jaccard is a nice compromise between the two.",
              "type": "string"
            },
            "enableModelingInsights": {
              "description": "Enable or disable metrics computation for the model.",
              "type": "boolean"
            },
            "splitterStrategy": {
              "description": "Defines the splitter strategy to be used by the build.\r\n            RandomSplitter splits the usage data in train and test sets based on the given\r\n            randomSplitterParameters value.\r\n            LastEventSplitter splits the usage data in train and test sets based on the last\r\n            transaction for a each user.",
              "type": "string"
            },
            "randomSplitterParameters": {
              "type": "object",
              "properties": {
                "testPercent": {
                  "format": "int32",
                  "description": "The percentage of data from the usage file that will be put in the test set\r\n            during splitting.",
                  "type": "integer"
                },
                "randomSeed": {
                  "format": "int32",
                  "description": "Number used to calculate the startig value of random sequence based on which\r\n            test set data is selected.",
                  "type": "integer"
                }
              },
              "description": "Specifies the parameters to be used for random splitter."
            },
            "dateSplitterParameters": {
              "type": "object",
              "properties": {
                "splitDate": {
                  "format": "date-time",
                  "description": "The split date at which the usage file data is put in the test set\r\n            during splitting.",
                  "type": "string"
                }
              },
              "description": "Specifies the parameters to be used for date splitter."
            },
            "popularItemBenchmarkWindow": {
              "format": "int32",
              "description": "Specifies the parameters to be used for computing popular items for modeling insights. (in number of days)",
              "type": "integer"
            }
          },
          "description": "Build parameters for build of type \"Fbt\""
        },
        "sar": {
          "type": "object",
          "properties": {
            "supportThreshold": {
              "format": "int32",
              "description": "Number of co-occurrences of items to be considered for modeling.\r\n            Value must an integer between 2 and 50. Default is 5.",
              "type": "integer"
            },
            "cooccurrenceUnit": {
              "description": "Indicates how to group usage events before counting co-occurrences.\r\n            Default: <value>CooccurrenceUnits.User</value>",
              "enum": [
                "User",
                "Timestamp"
              ],
              "type": "string"
            },
            "similarityFunction": {
              "description": "The similarity function to use in the model.\r\n            Default: Jaccard",
              "enum": [
                "Jaccard",
                "Concurrence",
                "Lift"
              ],
              "type": "string"
            },
            "enableColdItemPlacement": {
              "description": "Indicates whether to compute similarity of Cold to Warm/Cold Items based on catalog items' features. \r\n            Default: false",
              "type": "boolean"
            },
            "enableColdToColdRecommendations": {
              "description": "Indicates whether the similarity between pairs of cold items (catalog items without usage) should be computed. \r\n            If set to false, only similarity between cold and warm item will be computed, using catalog item features. \r\n            Note that this configuration is only relevant when enableColdItemSupport is set to true.\r\n            Default: false",
              "type": "boolean"
            },
            "enableModelingInsights": {
              "description": "Indicates whether to enable metrics computation for the model.\r\n            Default: false",
              "type": "boolean"
            },
            "enableU2I": {
              "description": "Allow the personalized recommendation a.k.a. U2I (user to item recommendations).",
              "type": "boolean"
            },
            "splitterStrategy": {
              "description": "Defines the splitter strategy to be used by the build.\r\n            Note that this configuration is only relevant when enableModelingInsights is set to true.",
              "enum": [
                "RandomSplitter",
                "LastEventSplitter",
                "DateSplitter"
              ],
              "type": "string"
            },
            "randomSplitterParameters": {
              "type": "object",
              "properties": {
                "testPercent": {
                  "format": "int32",
                  "description": "The percentage of data from the usage file that will be put in the test set\r\n            during splitting.",
                  "type": "integer"
                },
                "randomSeed": {
                  "format": "int32",
                  "description": "Number used to calculate the startig value of random sequence based on which\r\n            test set data is selected.",
                  "type": "integer"
                }
              },
              "description": "Specifies the parameters to be used for random splitter.\r\n            Note that this configuration is only relevant when splitterStrategy is set to RandomSplitter."
            },
            "dateSplitterParameters": {
              "type": "object",
              "properties": {
                "splitDate": {
                  "format": "date-time",
                  "description": "The split date at which the usage file data is put in the test set\r\n            during splitting.",
                  "type": "string"
                }
              },
              "description": "Specifies the parameters to be used for date splitter."
            },
            "popularItemBenchmarkWindow": {
              "format": "int32",
              "description": "Specifies the parameters to be used for computing popular items for modeling insights. (in number of days)",
              "type": "integer"
            },
            "enableUserAffinity": {
              "description": "For future use - Allows recommendations to use timestamps and event types.\r\n            Default: false",
              "type": "boolean"
            },
            "allowSeedItemsInRecommendations": {
              "description": "Allow seed items (items in the input or in the user history) to be returned as recommendation results.\r\n            Default: false",
              "type": "boolean"
            },
            "enableBackfilling": {
              "description": "Backfill with popular items when the system does not find sufficient recommendations.\r\n            Default: true",
              "type": "boolean"
            }
          },
          "description": "Build parameters for build of type \"SAR\""
        }
      },
      "description": "Parameters for build"
    }
  }
}
{
  "id": 0,
  "description": "string",
  "type": "string",
  "modelName": "string",
  "modelId": "string",
  "status": "string",
  "statusMessage": "string",
  "startDateTime": "string",
  "endDateTime": "string",
  "modifiedDateTime": "string",
  "buildParameters": {
    "ranking": {
      "numberOfModelIterations": 0,
      "numberOfModelDimensions": 0,
      "itemCutOffLowerBound": 0,
      "itemCutOffUpperBound": 0,
      "userCutOffLowerBound": 0,
      "userCutOffUpperBound": 0
    },
    "recommendation": {
      "numberOfModelIterations": 0,
      "numberOfModelDimensions": 0,
      "itemCutOffLowerBound": 0,
      "itemCutOffUpperBound": 0,
      "userCutOffLowerBound": 0,
      "userCutOffUpperBound": 0,
      "enableModelingInsights": true,
      "splitterStrategy": "string",
      "randomSplitterParameters": {
        "testPercent": 0,
        "randomSeed": 0
      },
      "dateSplitterParameters": {
        "splitDate": "string"
      },
      "popularItemBenchmarkWindow": 0,
      "useFeaturesInModel": true,
      "modelingFeatureList": "string",
      "allowColdItemPlacement": true,
      "enableFeatureCorrelation": true,
      "reasoningFeatureList": "string",
      "enableU2I": true
    },
    "fbt": {
      "supportThreshold": 0,
      "maxItemSetSize": 0,
      "minimalScore": 0.0,
      "similarityFunction": "string",
      "enableModelingInsights": true,
      "splitterStrategy": "string",
      "randomSplitterParameters": {
        "testPercent": 0,
        "randomSeed": 0
      },
      "dateSplitterParameters": {
        "splitDate": "string"
      },
      "popularItemBenchmarkWindow": 0
    },
    "sar": {
      "supportThreshold": 0,
      "cooccurrenceUnit": "User",
      "similarityFunction": "Jaccard",
      "enableColdItemPlacement": true,
      "enableColdToColdRecommendations": true,
      "enableModelingInsights": true,
      "enableU2I": true,
      "splitterStrategy": "RandomSplitter",
      "randomSplitterParameters": {
        "testPercent": 0,
        "randomSeed": 0
      },
      "dateSplitterParameters": {
        "splitDate": "string"
      },
      "popularItemBenchmarkWindow": 0,
      "enableUserAffinity": true,
      "allowSeedItemsInRecommendations": true,
      "enableBackfilling": true
    }
  }
}
{
  "type": "object",
  "properties": {
    "id": {
      "format": "int64",
      "description": "Unique build identifier",
      "type": "integer"
    },
    "description": {
      "description": "Description provided by user (BuildRequestInfo.Description)",
      "type": "string"
    },
    "type": {
      "description": "Type of build: Recommendation - 1, Ranking - 2, Fbt - 3",
      "type": "string"
    },
    "modelName": {
      "description": "Name of the Model associated this build",
      "type": "string"
    },
    "modelId": {
      "description": "ID of the Model associated this build",
      "type": "string"
    },
    "status": {
      "description": "Status of the build: NotStarted, Running, Cancelling, Cancelled, Succeeded, Failed",
      "type": "string"
    },
    "statusMessage": {
      "description": "Details if available about build status",
      "type": "string"
    },
    "startDateTime": {
      "description": "Build start time",
      "type": "string"
    },
    "endDateTime": {
      "description": "Build end time",
      "type": "string"
    },
    "modifiedDateTime": {
      "description": "Last build modified time",
      "type": "string"
    },
    "buildParameters": {
      "type": "object",
      "properties": {
        "ranking": {
          "type": "object",
          "properties": {
            "numberOfModelIterations": {
              "format": "int32",
              "description": "The number of iterations the model performs. \r\n            The higher the number, the better accuracy, but compute time will be higher.",
              "type": "integer"
            },
            "numberOfModelDimensions": {
              "format": "int32",
              "description": "The number of dimensions relates to the number of 'features' the model will try to find within your data. \r\n            Increasing the number of dimensions will allow better fine-tuning of the results into smaller clusters. \r\n            However, too many dimensions will prevent the model from finding correlations between items.",
              "type": "integer"
            },
            "itemCutOffLowerBound": {
              "format": "int32",
              "description": "Defines the item lower bound for usage condenser.",
              "type": "integer"
            },
            "itemCutOffUpperBound": {
              "format": "int32",
              "description": "Defines the item upper bound for usage condenser.",
              "type": "integer"
            },
            "userCutOffLowerBound": {
              "format": "int32",
              "description": "Defines the user lower bound for usage condenser.",
              "type": "integer"
            },
            "userCutOffUpperBound": {
              "format": "int32",
              "description": "Defines the user upper bound for usage condenser.",
              "type": "integer"
            }
          },
          "description": "Build parameters for build of type \"Ranking\""
        },
        "recommendation": {
          "type": "object",
          "properties": {
            "numberOfModelIterations": {
              "format": "int32",
              "description": "The number of iterations the model performs. \r\n            The higher the number, the better accuracy, but compute time will be higher.",
              "type": "integer"
            },
            "numberOfModelDimensions": {
              "format": "int32",
              "description": "The number of dimensions relates to the number of 'features' the model will try to find within your data. \r\n            Increasing the number of dimensions will allow better fine-tuning of the results into smaller clusters. \r\n            However, too many dimensions will prevent the model from finding correlations between items.",
              "type": "integer"
            },
            "itemCutOffLowerBound": {
              "format": "int32",
              "description": "Defines the item lower bound for usage condenser.",
              "type": "integer"
            },
            "itemCutOffUpperBound": {
              "format": "int32",
              "description": "Defines the item upper bound for usage condenser.",
              "type": "integer"
            },
            "userCutOffLowerBound": {
              "format": "int32",
              "description": "Defines the user lower bound for usage condenser.",
              "type": "integer"
            },
            "userCutOffUpperBound": {
              "format": "int32",
              "description": "Defines the user upper bound for usage condenser.",
              "type": "integer"
            },
            "enableModelingInsights": {
              "description": "Enable or disable metrics computation for the model.",
              "type": "boolean"
            },
            "splitterStrategy": {
              "description": "Defines the splitter strategy to be used by the build.\r\n            RandomSplitter splits the usage data in train and test sets based on the given\r\n            randomSplitterParameters value.\r\n            LastEventSplitter splits the usage data in train and test sets based on the last\r\n            transaction for a each user.",
              "type": "string"
            },
            "randomSplitterParameters": {
              "type": "object",
              "properties": {
                "testPercent": {
                  "format": "int32",
                  "description": "The percentage of data from the usage file that will be put in the test set\r\n            during splitting.",
                  "type": "integer"
                },
                "randomSeed": {
                  "format": "int32",
                  "description": "Number used to calculate the startig value of random sequence based on which\r\n            test set data is selected.",
                  "type": "integer"
                }
              },
              "description": "Specifies the parameters to be used for random splitter."
            },
            "dateSplitterParameters": {
              "type": "object",
              "properties": {
                "splitDate": {
                  "format": "date-time",
                  "description": "The split date at which the usage file data is put in the test set\r\n            during splitting.",
                  "type": "string"
                }
              },
              "description": "Specifies the parameters to be used for date splitter."
            },
            "popularItemBenchmarkWindow": {
              "format": "int32",
              "description": "Specifies the parameters to be used for computing popular items for modeling insights. (in number of days)",
              "type": "integer"
            },
            "useFeaturesInModel": {
              "description": "Indicates if features can be used in order to enhance the recommendation model.",
              "type": "boolean"
            },
            "modelingFeatureList": {
              "description": "Comma-separated list of feature names to be used during build.",
              "type": "string"
            },
            "allowColdItemPlacement": {
              "description": "Indicates if the recommendation should also push cold items via feature similarity.",
              "type": "boolean"
            },
            "enableFeatureCorrelation": {
              "description": "Indicates if features can be used in reasoning.",
              "type": "boolean"
            },
            "reasoningFeatureList": {
              "description": "Comma-separated list of feature names to be used for reasoning sentences (e.g. recommendation explanations).",
              "type": "string"
            },
            "enableU2I": {
              "description": "Allow the personalized recommendation a.k.a. U2I (user to item recommendations).",
              "type": "boolean"
            }
          },
          "description": "Build parameters for build of type \"Recommendation\""
        },
        "fbt": {
          "type": "object",
          "properties": {
            "supportThreshold": {
              "format": "int32",
              "description": "Number of co-occurrences of items to be considered for modeling.",
              "type": "integer"
            },
            "maxItemSetSize": {
              "format": "int32",
              "description": "Bound for number of items in a frequent set.",
              "type": "integer"
            },
            "minimalScore": {
              "format": "double",
              "description": "Minimal score that a frequent set should have in order to be included in the returned results.",
              "type": "number"
            },
            "similarityFunction": {
              "description": "Defines the similarity function to be used by the build. \r\n            Lift favors serendipity, Co-occurrence favors predictability, and Jaccard is a nice compromise between the two.",
              "type": "string"
            },
            "enableModelingInsights": {
              "description": "Enable or disable metrics computation for the model.",
              "type": "boolean"
            },
            "splitterStrategy": {
              "description": "Defines the splitter strategy to be used by the build.\r\n            RandomSplitter splits the usage data in train and test sets based on the given\r\n            randomSplitterParameters value.\r\n            LastEventSplitter splits the usage data in train and test sets based on the last\r\n            transaction for a each user.",
              "type": "string"
            },
            "randomSplitterParameters": {
              "type": "object",
              "properties": {
                "testPercent": {
                  "format": "int32",
                  "description": "The percentage of data from the usage file that will be put in the test set\r\n            during splitting.",
                  "type": "integer"
                },
                "randomSeed": {
                  "format": "int32",
                  "description": "Number used to calculate the startig value of random sequence based on which\r\n            test set data is selected.",
                  "type": "integer"
                }
              },
              "description": "Specifies the parameters to be used for random splitter."
            },
            "dateSplitterParameters": {
              "type": "object",
              "properties": {
                "splitDate": {
                  "format": "date-time",
                  "description": "The split date at which the usage file data is put in the test set\r\n            during splitting.",
                  "type": "string"
                }
              },
              "description": "Specifies the parameters to be used for date splitter."
            },
            "popularItemBenchmarkWindow": {
              "format": "int32",
              "description": "Specifies the parameters to be used for computing popular items for modeling insights. (in number of days)",
              "type": "integer"
            }
          },
          "description": "Build parameters for build of type \"Fbt\""
        },
        "sar": {
          "type": "object",
          "properties": {
            "supportThreshold": {
              "format": "int32",
              "description": "Number of co-occurrences of items to be considered for modeling.\r\n            Value must an integer between 2 and 50. Default is 5.",
              "type": "integer"
            },
            "cooccurrenceUnit": {
              "description": "Indicates how to group usage events before counting co-occurrences.\r\n            Default: <value>CooccurrenceUnits.User</value>",
              "enum": [
                "User",
                "Timestamp"
              ],
              "type": "string"
            },
            "similarityFunction": {
              "description": "The similarity function to use in the model.\r\n            Default: Jaccard",
              "enum": [
                "Jaccard",
                "Concurrence",
                "Lift"
              ],
              "type": "string"
            },
            "enableColdItemPlacement": {
              "description": "Indicates whether to compute similarity of Cold to Warm/Cold Items based on catalog items' features. \r\n            Default: false",
              "type": "boolean"
            },
            "enableColdToColdRecommendations": {
              "description": "Indicates whether the similarity between pairs of cold items (catalog items without usage) should be computed. \r\n            If set to false, only similarity between cold and warm item will be computed, using catalog item features. \r\n            Note that this configuration is only relevant when enableColdItemSupport is set to true.\r\n            Default: false",
              "type": "boolean"
            },
            "enableModelingInsights": {
              "description": "Indicates whether to enable metrics computation for the model.\r\n            Default: false",
              "type": "boolean"
            },
            "enableU2I": {
              "description": "Allow the personalized recommendation a.k.a. U2I (user to item recommendations).",
              "type": "boolean"
            },
            "splitterStrategy": {
              "description": "Defines the splitter strategy to be used by the build.\r\n            Note that this configuration is only relevant when enableModelingInsights is set to true.",
              "enum": [
                "RandomSplitter",
                "LastEventSplitter",
                "DateSplitter"
              ],
              "type": "string"
            },
            "randomSplitterParameters": {
              "type": "object",
              "properties": {
                "testPercent": {
                  "format": "int32",
                  "description": "The percentage of data from the usage file that will be put in the test set\r\n            during splitting.",
                  "type": "integer"
                },
                "randomSeed": {
                  "format": "int32",
                  "description": "Number used to calculate the startig value of random sequence based on which\r\n            test set data is selected.",
                  "type": "integer"
                }
              },
              "description": "Specifies the parameters to be used for random splitter.\r\n            Note that this configuration is only relevant when splitterStrategy is set to RandomSplitter."
            },
            "dateSplitterParameters": {
              "type": "object",
              "properties": {
                "splitDate": {
                  "format": "date-time",
                  "description": "The split date at which the usage file data is put in the test set\r\n            during splitting.",
                  "type": "string"
                }
              },
              "description": "Specifies the parameters to be used for date splitter."
            },
            "popularItemBenchmarkWindow": {
              "format": "int32",
              "description": "Specifies the parameters to be used for computing popular items for modeling insights. (in number of days)",
              "type": "integer"
            },
            "enableUserAffinity": {
              "description": "For future use - Allows recommendations to use timestamps and event types.\r\n            Default: false",
              "type": "boolean"
            },
            "allowSeedItemsInRecommendations": {
              "description": "Allow seed items (items in the input or in the user history) to be returned as recommendation results.\r\n            Default: false",
              "type": "boolean"
            },
            "enableBackfilling": {
              "description": "Backfill with popular items when the system does not find sufficient recommendations.\r\n            Default: true",
              "type": "boolean"
            }
          },
          "description": "Build parameters for build of type \"SAR\""
        }
      },
      "description": "Parameters for build"
    }
  }
}
<BuildInfo>
  <id>0</id>
  <description>string</description>
  <type>string</type>
  <modelName>string</modelName>
  <modelId>string</modelId>
  <status>string</status>
  <statusMessage>string</statusMessage>
  <startDateTime>string</startDateTime>
  <endDateTime>string</endDateTime>
  <modifiedDateTime>string</modifiedDateTime>
  <buildParameters>
    <ranking>
      <numberOfModelIterations>0</numberOfModelIterations>
      <numberOfModelDimensions>0</numberOfModelDimensions>
      <itemCutOffLowerBound>0</itemCutOffLowerBound>
      <itemCutOffUpperBound>0</itemCutOffUpperBound>
      <userCutOffLowerBound>0</userCutOffLowerBound>
      <userCutOffUpperBound>0</userCutOffUpperBound>
    </ranking>
    <recommendation>
      <numberOfModelIterations>0</numberOfModelIterations>
      <numberOfModelDimensions>0</numberOfModelDimensions>
      <itemCutOffLowerBound>0</itemCutOffLowerBound>
      <itemCutOffUpperBound>0</itemCutOffUpperBound>
      <userCutOffLowerBound>0</userCutOffLowerBound>
      <userCutOffUpperBound>0</userCutOffUpperBound>
      <enableModelingInsights>true</enableModelingInsights>
      <splitterStrategy>string</splitterStrategy>
      <randomSplitterParameters>
        <testPercent>0</testPercent>
        <randomSeed>0</randomSeed>
      </randomSplitterParameters>
      <dateSplitterParameters>
        <splitDate>string</splitDate>
      </dateSplitterParameters>
      <popularItemBenchmarkWindow>0</popularItemBenchmarkWindow>
      <useFeaturesInModel>true</useFeaturesInModel>
      <modelingFeatureList>string</modelingFeatureList>
      <allowColdItemPlacement>true</allowColdItemPlacement>
      <enableFeatureCorrelation>true</enableFeatureCorrelation>
      <reasoningFeatureList>string</reasoningFeatureList>
      <enableU2I>true</enableU2I>
    </recommendation>
    <fbt>
      <supportThreshold>0</supportThreshold>
      <maxItemSetSize>0</maxItemSetSize>
      <minimalScore>0</minimalScore>
      <similarityFunction>string</similarityFunction>
      <enableModelingInsights>true</enableModelingInsights>
      <splitterStrategy>string</splitterStrategy>
      <randomSplitterParameters>
        <testPercent>0</testPercent>
        <randomSeed>0</randomSeed>
      </randomSplitterParameters>
      <dateSplitterParameters>
        <splitDate>string</splitDate>
      </dateSplitterParameters>
      <popularItemBenchmarkWindow>0</popularItemBenchmarkWindow>
    </fbt>
    <sar>
      <supportThreshold>0</supportThreshold>
      <cooccurrenceUnit>User</cooccurrenceUnit>
      <similarityFunction>Jaccard</similarityFunction>
      <enableColdItemPlacement>true</enableColdItemPlacement>
      <enableColdToColdRecommendations>true</enableColdToColdRecommendations>
      <enableModelingInsights>true</enableModelingInsights>
      <enableU2I>true</enableU2I>
      <splitterStrategy>RandomSplitter</splitterStrategy>
      <randomSplitterParameters>
        <testPercent>0</testPercent>
        <randomSeed>0</randomSeed>
      </randomSplitterParameters>
      <dateSplitterParameters>
        <splitDate>string</splitDate>
      </dateSplitterParameters>
      <popularItemBenchmarkWindow>0</popularItemBenchmarkWindow>
      <enableUserAffinity>true</enableUserAffinity>
      <allowSeedItemsInRecommendations>true</allowSeedItemsInRecommendations>
      <enableBackfilling>true</enableBackfilling>
    </sar>
  </buildParameters>
</BuildInfo>
{
  "type": "object",
  "properties": {
    "id": {
      "format": "int64",
      "description": "Unique build identifier",
      "type": "integer"
    },
    "description": {
      "description": "Description provided by user (BuildRequestInfo.Description)",
      "type": "string"
    },
    "type": {
      "description": "Type of build: Recommendation - 1, Ranking - 2, Fbt - 3",
      "type": "string"
    },
    "modelName": {
      "description": "Name of the Model associated this build",
      "type": "string"
    },
    "modelId": {
      "description": "ID of the Model associated this build",
      "type": "string"
    },
    "status": {
      "description": "Status of the build: NotStarted, Running, Cancelling, Cancelled, Succeeded, Failed",
      "type": "string"
    },
    "statusMessage": {
      "description": "Details if available about build status",
      "type": "string"
    },
    "startDateTime": {
      "description": "Build start time",
      "type": "string"
    },
    "endDateTime": {
      "description": "Build end time",
      "type": "string"
    },
    "modifiedDateTime": {
      "description": "Last build modified time",
      "type": "string"
    },
    "buildParameters": {
      "type": "object",
      "properties": {
        "ranking": {
          "type": "object",
          "properties": {
            "numberOfModelIterations": {
              "format": "int32",
              "description": "The number of iterations the model performs. \r\n            The higher the number, the better accuracy, but compute time will be higher.",
              "type": "integer"
            },
            "numberOfModelDimensions": {
              "format": "int32",
              "description": "The number of dimensions relates to the number of 'features' the model will try to find within your data. \r\n            Increasing the number of dimensions will allow better fine-tuning of the results into smaller clusters. \r\n            However, too many dimensions will prevent the model from finding correlations between items.",
              "type": "integer"
            },
            "itemCutOffLowerBound": {
              "format": "int32",
              "description": "Defines the item lower bound for usage condenser.",
              "type": "integer"
            },
            "itemCutOffUpperBound": {
              "format": "int32",
              "description": "Defines the item upper bound for usage condenser.",
              "type": "integer"
            },
            "userCutOffLowerBound": {
              "format": "int32",
              "description": "Defines the user lower bound for usage condenser.",
              "type": "integer"
            },
            "userCutOffUpperBound": {
              "format": "int32",
              "description": "Defines the user upper bound for usage condenser.",
              "type": "integer"
            }
          },
          "description": "Build parameters for build of type \"Ranking\""
        },
        "recommendation": {
          "type": "object",
          "properties": {
            "numberOfModelIterations": {
              "format": "int32",
              "description": "The number of iterations the model performs. \r\n            The higher the number, the better accuracy, but compute time will be higher.",
              "type": "integer"
            },
            "numberOfModelDimensions": {
              "format": "int32",
              "description": "The number of dimensions relates to the number of 'features' the model will try to find within your data. \r\n            Increasing the number of dimensions will allow better fine-tuning of the results into smaller clusters. \r\n            However, too many dimensions will prevent the model from finding correlations between items.",
              "type": "integer"
            },
            "itemCutOffLowerBound": {
              "format": "int32",
              "description": "Defines the item lower bound for usage condenser.",
              "type": "integer"
            },
            "itemCutOffUpperBound": {
              "format": "int32",
              "description": "Defines the item upper bound for usage condenser.",
              "type": "integer"
            },
            "userCutOffLowerBound": {
              "format": "int32",
              "description": "Defines the user lower bound for usage condenser.",
              "type": "integer"
            },
            "userCutOffUpperBound": {
              "format": "int32",
              "description": "Defines the user upper bound for usage condenser.",
              "type": "integer"
            },
            "enableModelingInsights": {
              "description": "Enable or disable metrics computation for the model.",
              "type": "boolean"
            },
            "splitterStrategy": {
              "description": "Defines the splitter strategy to be used by the build.\r\n            RandomSplitter splits the usage data in train and test sets based on the given\r\n            randomSplitterParameters value.\r\n            LastEventSplitter splits the usage data in train and test sets based on the last\r\n            transaction for a each user.",
              "type": "string"
            },
            "randomSplitterParameters": {
              "type": "object",
              "properties": {
                "testPercent": {
                  "format": "int32",
                  "description": "The percentage of data from the usage file that will be put in the test set\r\n            during splitting.",
                  "type": "integer"
                },
                "randomSeed": {
                  "format": "int32",
                  "description": "Number used to calculate the startig value of random sequence based on which\r\n            test set data is selected.",
                  "type": "integer"
                }
              },
              "description": "Specifies the parameters to be used for random splitter."
            },
            "dateSplitterParameters": {
              "type": "object",
              "properties": {
                "splitDate": {
                  "format": "date-time",
                  "description": "The split date at which the usage file data is put in the test set\r\n            during splitting.",
                  "type": "string"
                }
              },
              "description": "Specifies the parameters to be used for date splitter."
            },
            "popularItemBenchmarkWindow": {
              "format": "int32",
              "description": "Specifies the parameters to be used for computing popular items for modeling insights. (in number of days)",
              "type": "integer"
            },
            "useFeaturesInModel": {
              "description": "Indicates if features can be used in order to enhance the recommendation model.",
              "type": "boolean"
            },
            "modelingFeatureList": {
              "description": "Comma-separated list of feature names to be used during build.",
              "type": "string"
            },
            "allowColdItemPlacement": {
              "description": "Indicates if the recommendation should also push cold items via feature similarity.",
              "type": "boolean"
            },
            "enableFeatureCorrelation": {
              "description": "Indicates if features can be used in reasoning.",
              "type": "boolean"
            },
            "reasoningFeatureList": {
              "description": "Comma-separated list of feature names to be used for reasoning sentences (e.g. recommendation explanations).",
              "type": "string"
            },
            "enableU2I": {
              "description": "Allow the personalized recommendation a.k.a. U2I (user to item recommendations).",
              "type": "boolean"
            }
          },
          "description": "Build parameters for build of type \"Recommendation\""
        },
        "fbt": {
          "type": "object",
          "properties": {
            "supportThreshold": {
              "format": "int32",
              "description": "Number of co-occurrences of items to be considered for modeling.",
              "type": "integer"
            },
            "maxItemSetSize": {
              "format": "int32",
              "description": "Bound for number of items in a frequent set.",
              "type": "integer"
            },
            "minimalScore": {
              "format": "double",
              "description": "Minimal score that a frequent set should have in order to be included in the returned results.",
              "type": "number"
            },
            "similarityFunction": {
              "description": "Defines the similarity function to be used by the build. \r\n            Lift favors serendipity, Co-occurrence favors predictability, and Jaccard is a nice compromise between the two.",
              "type": "string"
            },
            "enableModelingInsights": {
              "description": "Enable or disable metrics computation for the model.",
              "type": "boolean"
            },
            "splitterStrategy": {
              "description": "Defines the splitter strategy to be used by the build.\r\n            RandomSplitter splits the usage data in train and test sets based on the given\r\n            randomSplitterParameters value.\r\n            LastEventSplitter splits the usage data in train and test sets based on the last\r\n            transaction for a each user.",
              "type": "string"
            },
            "randomSplitterParameters": {
              "type": "object",
              "properties": {
                "testPercent": {
                  "format": "int32",
                  "description": "The percentage of data from the usage file that will be put in the test set\r\n            during splitting.",
                  "type": "integer"
                },
                "randomSeed": {
                  "format": "int32",
                  "description": "Number used to calculate the startig value of random sequence based on which\r\n            test set data is selected.",
                  "type": "integer"
                }
              },
              "description": "Specifies the parameters to be used for random splitter."
            },
            "dateSplitterParameters": {
              "type": "object",
              "properties": {
                "splitDate": {
                  "format": "date-time",
                  "description": "The split date at which the usage file data is put in the test set\r\n            during splitting.",
                  "type": "string"
                }
              },
              "description": "Specifies the parameters to be used for date splitter."
            },
            "popularItemBenchmarkWindow": {
              "format": "int32",
              "description": "Specifies the parameters to be used for computing popular items for modeling insights. (in number of days)",
              "type": "integer"
            }
          },
          "description": "Build parameters for build of type \"Fbt\""
        },
        "sar": {
          "type": "object",
          "properties": {
            "supportThreshold": {
              "format": "int32",
              "description": "Number of co-occurrences of items to be considered for modeling.\r\n            Value must an integer between 2 and 50. Default is 5.",
              "type": "integer"
            },
            "cooccurrenceUnit": {
              "description": "Indicates how to group usage events before counting co-occurrences.\r\n            Default: <value>CooccurrenceUnits.User</value>",
              "enum": [
                "User",
                "Timestamp"
              ],
              "type": "string"
            },
            "similarityFunction": {
              "description": "The similarity function to use in the model.\r\n            Default: Jaccard",
              "enum": [
                "Jaccard",
                "Concurrence",
                "Lift"
              ],
              "type": "string"
            },
            "enableColdItemPlacement": {
              "description": "Indicates whether to compute similarity of Cold to Warm/Cold Items based on catalog items' features. \r\n            Default: false",
              "type": "boolean"
            },
            "enableColdToColdRecommendations": {
              "description": "Indicates whether the similarity between pairs of cold items (catalog items without usage) should be computed. \r\n            If set to false, only similarity between cold and warm item will be computed, using catalog item features. \r\n            Note that this configuration is only relevant when enableColdItemSupport is set to true.\r\n            Default: false",
              "type": "boolean"
            },
            "enableModelingInsights": {
              "description": "Indicates whether to enable metrics computation for the model.\r\n            Default: false",
              "type": "boolean"
            },
            "enableU2I": {
              "description": "Allow the personalized recommendation a.k.a. U2I (user to item recommendations).",
              "type": "boolean"
            },
            "splitterStrategy": {
              "description": "Defines the splitter strategy to be used by the build.\r\n            Note that this configuration is only relevant when enableModelingInsights is set to true.",
              "enum": [
                "RandomSplitter",
                "LastEventSplitter",
                "DateSplitter"
              ],
              "type": "string"
            },
            "randomSplitterParameters": {
              "type": "object",
              "properties": {
                "testPercent": {
                  "format": "int32",
                  "description": "The percentage of data from the usage file that will be put in the test set\r\n            during splitting.",
                  "type": "integer"
                },
                "randomSeed": {
                  "format": "int32",
                  "description": "Number used to calculate the startig value of random sequence based on which\r\n            test set data is selected.",
                  "type": "integer"
                }
              },
              "description": "Specifies the parameters to be used for random splitter.\r\n            Note that this configuration is only relevant when splitterStrategy is set to RandomSplitter."
            },
            "dateSplitterParameters": {
              "type": "object",
              "properties": {
                "splitDate": {
                  "format": "date-time",
                  "description": "The split date at which the usage file data is put in the test set\r\n            during splitting.",
                  "type": "string"
                }
              },
              "description": "Specifies the parameters to be used for date splitter."
            },
            "popularItemBenchmarkWindow": {
              "format": "int32",
              "description": "Specifies the parameters to be used for computing popular items for modeling insights. (in number of days)",
              "type": "integer"
            },
            "enableUserAffinity": {
              "description": "For future use - Allows recommendations to use timestamps and event types.\r\n            Default: false",
              "type": "boolean"
            },
            "allowSeedItemsInRecommendations": {
              "description": "Allow seed items (items in the input or in the user history) to be returned as recommendation results.\r\n            Default: false",
              "type": "boolean"
            },
            "enableBackfilling": {
              "description": "Backfill with popular items when the system does not find sufficient recommendations.\r\n            Default: true",
              "type": "boolean"
            }
          },
          "description": "Build parameters for build of type \"SAR\""
        }
      },
      "description": "Parameters for build"
    }
  }
}
<BuildInfo>
  <id>0</id>
  <description>string</description>
  <type>string</type>
  <modelName>string</modelName>
  <modelId>string</modelId>
  <status>string</status>
  <statusMessage>string</statusMessage>
  <startDateTime>string</startDateTime>
  <endDateTime>string</endDateTime>
  <modifiedDateTime>string</modifiedDateTime>
  <buildParameters>
    <ranking>
      <numberOfModelIterations>0</numberOfModelIterations>
      <numberOfModelDimensions>0</numberOfModelDimensions>
      <itemCutOffLowerBound>0</itemCutOffLowerBound>
      <itemCutOffUpperBound>0</itemCutOffUpperBound>
      <userCutOffLowerBound>0</userCutOffLowerBound>
      <userCutOffUpperBound>0</userCutOffUpperBound>
    </ranking>
    <recommendation>
      <numberOfModelIterations>0</numberOfModelIterations>
      <numberOfModelDimensions>0</numberOfModelDimensions>
      <itemCutOffLowerBound>0</itemCutOffLowerBound>
      <itemCutOffUpperBound>0</itemCutOffUpperBound>
      <userCutOffLowerBound>0</userCutOffLowerBound>
      <userCutOffUpperBound>0</userCutOffUpperBound>
      <enableModelingInsights>true</enableModelingInsights>
      <splitterStrategy>string</splitterStrategy>
      <randomSplitterParameters>
        <testPercent>0</testPercent>
        <randomSeed>0</randomSeed>
      </randomSplitterParameters>
      <dateSplitterParameters>
        <splitDate>string</splitDate>
      </dateSplitterParameters>
      <popularItemBenchmarkWindow>0</popularItemBenchmarkWindow>
      <useFeaturesInModel>true</useFeaturesInModel>
      <modelingFeatureList>string</modelingFeatureList>
      <allowColdItemPlacement>true</allowColdItemPlacement>
      <enableFeatureCorrelation>true</enableFeatureCorrelation>
      <reasoningFeatureList>string</reasoningFeatureList>
      <enableU2I>true</enableU2I>
    </recommendation>
    <fbt>
      <supportThreshold>0</supportThreshold>
      <maxItemSetSize>0</maxItemSetSize>
      <minimalScore>0</minimalScore>
      <similarityFunction>string</similarityFunction>
      <enableModelingInsights>true</enableModelingInsights>
      <splitterStrategy>string</splitterStrategy>
      <randomSplitterParameters>
        <testPercent>0</testPercent>
        <randomSeed>0</randomSeed>
      </randomSplitterParameters>
      <dateSplitterParameters>
        <splitDate>string</splitDate>
      </dateSplitterParameters>
      <popularItemBenchmarkWindow>0</popularItemBenchmarkWindow>
    </fbt>
    <sar>
      <supportThreshold>0</supportThreshold>
      <cooccurrenceUnit>User</cooccurrenceUnit>
      <similarityFunction>Jaccard</similarityFunction>
      <enableColdItemPlacement>true</enableColdItemPlacement>
      <enableColdToColdRecommendations>true</enableColdToColdRecommendations>
      <enableModelingInsights>true</enableModelingInsights>
      <enableU2I>true</enableU2I>
      <splitterStrategy>RandomSplitter</splitterStrategy>
      <randomSplitterParameters>
        <testPercent>0</testPercent>
        <randomSeed>0</randomSeed>
      </randomSplitterParameters>
      <dateSplitterParameters>
        <splitDate>string</splitDate>
      </dateSplitterParameters>
      <popularItemBenchmarkWindow>0</popularItemBenchmarkWindow>
      <enableUserAffinity>true</enableUserAffinity>
      <allowSeedItemsInRecommendations>true</allowSeedItemsInRecommendations>
      <enableBackfilling>true</enableBackfilling>
    </sar>
  </buildParameters>
</BuildInfo>
{
  "type": "object",
  "properties": {
    "id": {
      "format": "int64",
      "description": "Unique build identifier",
      "type": "integer"
    },
    "description": {
      "description": "Description provided by user (BuildRequestInfo.Description)",
      "type": "string"
    },
    "type": {
      "description": "Type of build: Recommendation - 1, Ranking - 2, Fbt - 3",
      "type": "string"
    },
    "modelName": {
      "description": "Name of the Model associated this build",
      "type": "string"
    },
    "modelId": {
      "description": "ID of the Model associated this build",
      "type": "string"
    },
    "status": {
      "description": "Status of the build: NotStarted, Running, Cancelling, Cancelled, Succeeded, Failed",
      "type": "string"
    },
    "statusMessage": {
      "description": "Details if available about build status",
      "type": "string"
    },
    "startDateTime": {
      "description": "Build start time",
      "type": "string"
    },
    "endDateTime": {
      "description": "Build end time",
      "type": "string"
    },
    "modifiedDateTime": {
      "description": "Last build modified time",
      "type": "string"
    },
    "buildParameters": {
      "type": "object",
      "properties": {
        "ranking": {
          "type": "object",
          "properties": {
            "numberOfModelIterations": {
              "format": "int32",
              "description": "The number of iterations the model performs. \r\n            The higher the number, the better accuracy, but compute time will be higher.",
              "type": "integer"
            },
            "numberOfModelDimensions": {
              "format": "int32",
              "description": "The number of dimensions relates to the number of 'features' the model will try to find within your data. \r\n            Increasing the number of dimensions will allow better fine-tuning of the results into smaller clusters. \r\n            However, too many dimensions will prevent the model from finding correlations between items.",
              "type": "integer"
            },
            "itemCutOffLowerBound": {
              "format": "int32",
              "description": "Defines the item lower bound for usage condenser.",
              "type": "integer"
            },
            "itemCutOffUpperBound": {
              "format": "int32",
              "description": "Defines the item upper bound for usage condenser.",
              "type": "integer"
            },
            "userCutOffLowerBound": {
              "format": "int32",
              "description": "Defines the user lower bound for usage condenser.",
              "type": "integer"
            },
            "userCutOffUpperBound": {
              "format": "int32",
              "description": "Defines the user upper bound for usage condenser.",
              "type": "integer"
            }
          },
          "description": "Build parameters for build of type \"Ranking\""
        },
        "recommendation": {
          "type": "object",
          "properties": {
            "numberOfModelIterations": {
              "format": "int32",
              "description": "The number of iterations the model performs. \r\n            The higher the number, the better accuracy, but compute time will be higher.",
              "type": "integer"
            },
            "numberOfModelDimensions": {
              "format": "int32",
              "description": "The number of dimensions relates to the number of 'features' the model will try to find within your data. \r\n            Increasing the number of dimensions will allow better fine-tuning of the results into smaller clusters. \r\n            However, too many dimensions will prevent the model from finding correlations between items.",
              "type": "integer"
            },
            "itemCutOffLowerBound": {
              "format": "int32",
              "description": "Defines the item lower bound for usage condenser.",
              "type": "integer"
            },
            "itemCutOffUpperBound": {
              "format": "int32",
              "description": "Defines the item upper bound for usage condenser.",
              "type": "integer"
            },
            "userCutOffLowerBound": {
              "format": "int32",
              "description": "Defines the user lower bound for usage condenser.",
              "type": "integer"
            },
            "userCutOffUpperBound": {
              "format": "int32",
              "description": "Defines the user upper bound for usage condenser.",
              "type": "integer"
            },
            "enableModelingInsights": {
              "description": "Enable or disable metrics computation for the model.",
              "type": "boolean"
            },
            "splitterStrategy": {
              "description": "Defines the splitter strategy to be used by the build.\r\n            RandomSplitter splits the usage data in train and test sets based on the given\r\n            randomSplitterParameters value.\r\n            LastEventSplitter splits the usage data in train and test sets based on the last\r\n            transaction for a each user.",
              "type": "string"
            },
            "randomSplitterParameters": {
              "type": "object",
              "properties": {
                "testPercent": {
                  "format": "int32",
                  "description": "The percentage of data from the usage file that will be put in the test set\r\n            during splitting.",
                  "type": "integer"
                },
                "randomSeed": {
                  "format": "int32",
                  "description": "Number used to calculate the startig value of random sequence based on which\r\n            test set data is selected.",
                  "type": "integer"
                }
              },
              "description": "Specifies the parameters to be used for random splitter."
            },
            "dateSplitterParameters": {
              "type": "object",
              "properties": {
                "splitDate": {
                  "format": "date-time",
                  "description": "The split date at which the usage file data is put in the test set\r\n            during splitting.",
                  "type": "string"
                }
              },
              "description": "Specifies the parameters to be used for date splitter."
            },
            "popularItemBenchmarkWindow": {
              "format": "int32",
              "description": "Specifies the parameters to be used for computing popular items for modeling insights. (in number of days)",
              "type": "integer"
            },
            "useFeaturesInModel": {
              "description": "Indicates if features can be used in order to enhance the recommendation model.",
              "type": "boolean"
            },
            "modelingFeatureList": {
              "description": "Comma-separated list of feature names to be used during build.",
              "type": "string"
            },
            "allowColdItemPlacement": {
              "description": "Indicates if the recommendation should also push cold items via feature similarity.",
              "type": "boolean"
            },
            "enableFeatureCorrelation": {
              "description": "Indicates if features can be used in reasoning.",
              "type": "boolean"
            },
            "reasoningFeatureList": {
              "description": "Comma-separated list of feature names to be used for reasoning sentences (e.g. recommendation explanations).",
              "type": "string"
            },
            "enableU2I": {
              "description": "Allow the personalized recommendation a.k.a. U2I (user to item recommendations).",
              "type": "boolean"
            }
          },
          "description": "Build parameters for build of type \"Recommendation\""
        },
        "fbt": {
          "type": "object",
          "properties": {
            "supportThreshold": {
              "format": "int32",
              "description": "Number of co-occurrences of items to be considered for modeling.",
              "type": "integer"
            },
            "maxItemSetSize": {
              "format": "int32",
              "description": "Bound for number of items in a frequent set.",
              "type": "integer"
            },
            "minimalScore": {
              "format": "double",
              "description": "Minimal score that a frequent set should have in order to be included in the returned results.",
              "type": "number"
            },
            "similarityFunction": {
              "description": "Defines the similarity function to be used by the build. \r\n            Lift favors serendipity, Co-occurrence favors predictability, and Jaccard is a nice compromise between the two.",
              "type": "string"
            },
            "enableModelingInsights": {
              "description": "Enable or disable metrics computation for the model.",
              "type": "boolean"
            },
            "splitterStrategy": {
              "description": "Defines the splitter strategy to be used by the build.\r\n            RandomSplitter splits the usage data in train and test sets based on the given\r\n            randomSplitterParameters value.\r\n            LastEventSplitter splits the usage data in train and test sets based on the last\r\n            transaction for a each user.",
              "type": "string"
            },
            "randomSplitterParameters": {
              "type": "object",
              "properties": {
                "testPercent": {
                  "format": "int32",
                  "description": "The percentage of data from the usage file that will be put in the test set\r\n            during splitting.",
                  "type": "integer"
                },
                "randomSeed": {
                  "format": "int32",
                  "description": "Number used to calculate the startig value of random sequence based on which\r\n            test set data is selected.",
                  "type": "integer"
                }
              },
              "description": "Specifies the parameters to be used for random splitter."
            },
            "dateSplitterParameters": {
              "type": "object",
              "properties": {
                "splitDate": {
                  "format": "date-time",
                  "description": "The split date at which the usage file data is put in the test set\r\n            during splitting.",
                  "type": "string"
                }
              },
              "description": "Specifies the parameters to be used for date splitter."
            },
            "popularItemBenchmarkWindow": {
              "format": "int32",
              "description": "Specifies the parameters to be used for computing popular items for modeling insights. (in number of days)",
              "type": "integer"
            }
          },
          "description": "Build parameters for build of type \"Fbt\""
        },
        "sar": {
          "type": "object",
          "properties": {
            "supportThreshold": {
              "format": "int32",
              "description": "Number of co-occurrences of items to be considered for modeling.\r\n            Value must an integer between 2 and 50. Default is 5.",
              "type": "integer"
            },
            "cooccurrenceUnit": {
              "description": "Indicates how to group usage events before counting co-occurrences.\r\n            Default: <value>CooccurrenceUnits.User</value>",
              "enum": [
                "User",
                "Timestamp"
              ],
              "type": "string"
            },
            "similarityFunction": {
              "description": "The similarity function to use in the model.\r\n            Default: Jaccard",
              "enum": [
                "Jaccard",
                "Concurrence",
                "Lift"
              ],
              "type": "string"
            },
            "enableColdItemPlacement": {
              "description": "Indicates whether to compute similarity of Cold to Warm/Cold Items based on catalog items' features. \r\n            Default: false",
              "type": "boolean"
            },
            "enableColdToColdRecommendations": {
              "description": "Indicates whether the similarity between pairs of cold items (catalog items without usage) should be computed. \r\n            If set to false, only similarity between cold and warm item will be computed, using catalog item features. \r\n            Note that this configuration is only relevant when enableColdItemSupport is set to true.\r\n            Default: false",
              "type": "boolean"
            },
            "enableModelingInsights": {
              "description": "Indicates whether to enable metrics computation for the model.\r\n            Default: false",
              "type": "boolean"
            },
            "enableU2I": {
              "description": "Allow the personalized recommendation a.k.a. U2I (user to item recommendations).",
              "type": "boolean"
            },
            "splitterStrategy": {
              "description": "Defines the splitter strategy to be used by the build.\r\n            Note that this configuration is only relevant when enableModelingInsights is set to true.",
              "enum": [
                "RandomSplitter",
                "LastEventSplitter",
                "DateSplitter"
              ],
              "type": "string"
            },
            "randomSplitterParameters": {
              "type": "object",
              "properties": {
                "testPercent": {
                  "format": "int32",
                  "description": "The percentage of data from the usage file that will be put in the test set\r\n            during splitting.",
                  "type": "integer"
                },
                "randomSeed": {
                  "format": "int32",
                  "description": "Number used to calculate the startig value of random sequence based on which\r\n            test set data is selected.",
                  "type": "integer"
                }
              },
              "description": "Specifies the parameters to be used for random splitter.\r\n            Note that this configuration is only relevant when splitterStrategy is set to RandomSplitter."
            },
            "dateSplitterParameters": {
              "type": "object",
              "properties": {
                "splitDate": {
                  "format": "date-time",
                  "description": "The split date at which the usage file data is put in the test set\r\n            during splitting.",
                  "type": "string"
                }
              },
              "description": "Specifies the parameters to be used for date splitter."
            },
            "popularItemBenchmarkWindow": {
              "format": "int32",
              "description": "Specifies the parameters to be used for computing popular items for modeling insights. (in number of days)",
              "type": "integer"
            },
            "enableUserAffinity": {
              "description": "For future use - Allows recommendations to use timestamps and event types.\r\n            Default: false",
              "type": "boolean"
            },
            "allowSeedItemsInRecommendations": {
              "description": "Allow seed items (items in the input or in the user history) to be returned as recommendation results.\r\n            Default: false",
              "type": "boolean"
            },
            "enableBackfilling": {
              "description": "Backfill with popular items when the system does not find sufficient recommendations.\r\n            Default: true",
              "type": "boolean"
            }
          },
          "description": "Build parameters for build of type \"SAR\""
        }
      },
      "description": "Parameters for build"
    }
  }
}

Response 400

Code samples

@ECHO OFF

curl -v -X GET "https://westus.api.cognitive.microsoft.com/recommendations/v4.0/models/{modelId}/builds/{buildId}"
-H "Ocp-Apim-Subscription-Key: {subscription key}"

--data-ascii "{body}" 
using System;
using System.Net.Http.Headers;
using System.Text;
using System.Net.Http;
using System.Web;

namespace CSHttpClientSample
{
    static class Program
    {
        static void Main()
        {
            MakeRequest();
            Console.WriteLine("Hit ENTER to exit...");
            Console.ReadLine();
        }
        
        static async void MakeRequest()
        {
            var client = new HttpClient();
            var queryString = HttpUtility.ParseQueryString(string.Empty);

            // Request headers
            client.DefaultRequestHeaders.Add("Ocp-Apim-Subscription-Key", "{subscription key}");

            var uri = "https://westus.api.cognitive.microsoft.com/recommendations/v4.0/models/{modelId}/builds/{buildId}?" + queryString;

            var response = await client.GetAsync(uri);
        }
    }
}	
// // This sample uses the Apache HTTP client from HTTP Components (http://hc.apache.org/httpcomponents-client-ga/)
import java.net.URI;
import org.apache.http.HttpEntity;
import org.apache.http.HttpResponse;
import org.apache.http.client.HttpClient;
import org.apache.http.client.methods.HttpGet;
import org.apache.http.client.utils.URIBuilder;
import org.apache.http.impl.client.HttpClients;
import org.apache.http.util.EntityUtils;

public class JavaSample 
{
    public static void main(String[] args) 
    {
        HttpClient httpclient = HttpClients.createDefault();

        try
        {
            URIBuilder builder = new URIBuilder("https://westus.api.cognitive.microsoft.com/recommendations/v4.0/models/{modelId}/builds/{buildId}");


            URI uri = builder.build();
            HttpGet request = new HttpGet(uri);
            request.setHeader("Ocp-Apim-Subscription-Key", "{subscription key}");


            // Request body
            StringEntity reqEntity = new StringEntity("{body}");
            request.setEntity(reqEntity);

            HttpResponse response = httpclient.execute(request);
            HttpEntity entity = response.getEntity();

            if (entity != null) 
            {
                System.out.println(EntityUtils.toString(entity));
            }
        }
        catch (Exception e)
        {
            System.out.println(e.getMessage());
        }
    }
}

<!DOCTYPE html>
<html>
<head>
    <title>JSSample</title>
    <script src="http://ajax.googleapis.com/ajax/libs/jquery/1.9.0/jquery.min.js"></script>
</head>
<body>

<script type="text/javascript">
    $(function() {
        var params = {
            // Request parameters
        };
      
        $.ajax({
            url: "https://westus.api.cognitive.microsoft.com/recommendations/v4.0/models/{modelId}/builds/{buildId}?" + $.param(params),
            beforeSend: function(xhrObj){
                // Request headers
                xhrObj.setRequestHeader("Ocp-Apim-Subscription-Key","{subscription key}");
            },
            type: "GET",
            // Request body
            data: "{body}",
        })
        .done(function(data) {
            alert("success");
        })
        .fail(function() {
            alert("error");
        });
    });
</script>
</body>
</html>
#import <Foundation/Foundation.h>

int main(int argc, const char * argv[])
{
    NSAutoreleasePool * pool = [[NSAutoreleasePool alloc] init];
    
    NSString* path = @"https://westus.api.cognitive.microsoft.com/recommendations/v4.0/models/{modelId}/builds/{buildId}";
    NSArray* array = @[
                         // Request parameters
                         @"entities=true",
                      ];
    
    NSString* string = [array componentsJoinedByString:@"&"];
    path = [path stringByAppendingFormat:@"?%@", string];

    NSLog(@"%@", path);

    NSMutableURLRequest* _request = [NSMutableURLRequest requestWithURL:[NSURL URLWithString:path]];
    [_request setHTTPMethod:@"GET"];
    // Request headers
    [_request setValue:@"{subscription key}" forHTTPHeaderField:@"Ocp-Apim-Subscription-Key"];
    // Request body
    [_request setHTTPBody:[@"{body}" dataUsingEncoding:NSUTF8StringEncoding]];
    
    NSURLResponse *response = nil;
    NSError *error = nil;
    NSData* _connectionData = [NSURLConnection sendSynchronousRequest:_request returningResponse:&response error:&error];

    if (nil != error)
    {
        NSLog(@"Error: %@", error);
    }
    else
    {
        NSError* error = nil;
        NSMutableDictionary* json = nil;
        NSString* dataString = [[NSString alloc] initWithData:_connectionData encoding:NSUTF8StringEncoding];
        NSLog(@"%@", dataString);
        
        if (nil != _connectionData)
        {
            json = [NSJSONSerialization JSONObjectWithData:_connectionData options:NSJSONReadingMutableContainers error:&error];
        }
        
        if (error || !json)
        {
            NSLog(@"Could not parse loaded json with error:%@", error);
        }
        
        NSLog(@"%@", json);
        _connectionData = nil;
    }
    
    [pool drain];

    return 0;
}
<?php
// This sample uses the Apache HTTP client from HTTP Components (http://hc.apache.org/httpcomponents-client-ga/)
require_once 'HTTP/Request2.php';

$request = new Http_Request2('https://westus.api.cognitive.microsoft.com/recommendations/v4.0/models/{modelId}/builds/{buildId}');
$url = $request->getUrl();

$headers = array(
    // Request headers
    'Ocp-Apim-Subscription-Key' => '{subscription key}',
);

$request->setHeader($headers);

$parameters = array(
    // Request parameters
);

$url->setQueryVariables($parameters);

$request->setMethod(HTTP_Request2::METHOD_GET);

// Request body
$request->setBody("{body}");

try
{
    $response = $request->send();
    echo $response->getBody();
}
catch (HttpException $ex)
{
    echo $ex;
}

?>
########### Python 2.7 #############
import httplib, urllib, base64

headers = {
    # Request headers
    'Ocp-Apim-Subscription-Key': '{subscription key}',
}

params = urllib.urlencode({
})

try:
    conn = httplib.HTTPSConnection('westus.api.cognitive.microsoft.com')
    conn.request("GET", "/recommendations/v4.0/models/{modelId}/builds/{buildId}?%s" % params, "{body}", headers)
    response = conn.getresponse()
    data = response.read()
    print(data)
    conn.close()
except Exception as e:
    print("[Errno {0}] {1}".format(e.errno, e.strerror))

####################################

########### Python 3.2 #############
import http.client, urllib.request, urllib.parse, urllib.error, base64

headers = {
    # Request headers
    'Ocp-Apim-Subscription-Key': '{subscription key}',
}

params = urllib.parse.urlencode({
})

try:
    conn = http.client.HTTPSConnection('westus.api.cognitive.microsoft.com')
    conn.request("GET", "/recommendations/v4.0/models/{modelId}/builds/{buildId}?%s" % params, "{body}", headers)
    response = conn.getresponse()
    data = response.read()
    print(data)
    conn.close()
except Exception as e:
    print("[Errno {0}] {1}".format(e.errno, e.strerror))

####################################
require 'net/http'

uri = URI('https://westus.api.cognitive.microsoft.com/recommendations/v4.0/models/{modelId}/builds/{buildId}')
uri.query = URI.encode_www_form({
})

request = Net::HTTP::Get.new(uri.request_uri)
# Request headers
request['Ocp-Apim-Subscription-Key'] = '{subscription key}'
# Request body
request.body = "{body}"

response = Net::HTTP.start(uri.host, uri.port, :use_ssl => uri.scheme == 'https') do |http|
    http.request(request)
end

puts response.body