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 metrics

Return metrics such as precision and diversity for a given build.

As part of the precision and diversity metrics evaluation, the system finds a sample of users, and then the transactions for those users are split into two groups: the training dataset and the test dataset. In order to get metrics, you should have set the enableModelingInsights parameter to true at build time.

Learn more about build metrics on the offline evaluation section of the Build Types and Model Quality documentation.

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

{
  "precisionItemRecommend": {
    "precisionMetrics": [
      {
        "k": 0,
        "percentage": 0.0,
        "usersInTest": 0,
        "usersConsidered": 0,
        "usersNotConsidered": 0
      }
    ],
    "error": "string"
  },
  "precisionUserRecommend": {
    "precisionMetrics": [
      {
        "k": 0,
        "percentage": 0.0,
        "usersInTest": 0,
        "usersConsidered": 0,
        "usersNotConsidered": 0
      }
    ],
    "error": "string"
  },
  "precisionPopularItemRecommend": {
    "precisionMetrics": [
      {
        "k": 0,
        "percentage": 0.0,
        "usersInTest": 0,
        "usersConsidered": 0,
        "usersNotConsidered": 0
      }
    ],
    "error": "string"
  },
  "diversityItemRecommend": {
    "percentileBuckets": [
      {
        "min": 0,
        "max": 0,
        "percentage": 0.0
      }
    ],
    "totalItemsRecommended": 0,
    "uniqueItemsRecommended": 0,
    "uniqueItemsInTrainSet": 0,
    "error": "string"
  },
  "diversityUserRecommend": {
    "percentileBuckets": [
      {
        "min": 0,
        "max": 0,
        "percentage": 0.0
      }
    ],
    "totalItemsRecommended": 0,
    "uniqueItemsRecommended": 0,
    "uniqueItemsInTrainSet": 0,
    "error": "string"
  }
}
{
  "type": "object",
  "properties": {
    "precisionItemRecommend": {
      "type": "object",
      "properties": {
        "precisionMetrics": {
          "description": "Precision metrics that are computed for the test/train dataset.",
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "k": {
                "format": "int32",
                "description": "The value K used to calculate the metric values.",
                "type": "integer"
              },
              "percentage": {
                "format": "double",
                "description": "Precision@K percentage.",
                "type": "number"
              },
              "usersInTest": {
                "format": "int32",
                "description": "The total number of users in the test dataset.",
                "type": "integer"
              },
              "usersConsidered": {
                "format": "int32",
                "description": "A user is only considered if the system recommended at least K items based on the model generated using the training dataset.",
                "type": "integer"
              },
              "usersNotConsidered": {
                "format": "int32",
                "description": "Any users not considered; the users that did not receive at least K recommended items.",
                "type": "integer"
              }
            }
          }
        },
        "error": {
          "description": "Error message to indicate reason in failure cases.",
          "type": "string"
        }
      },
      "description": "Precision metrics for the build based on single item recommendations."
    },
    "precisionUserRecommend": {
      "type": "object",
      "properties": {
        "precisionMetrics": {
          "description": "Precision metrics that are computed for the test/train dataset.",
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "k": {
                "format": "int32",
                "description": "The value K used to calculate the metric values.",
                "type": "integer"
              },
              "percentage": {
                "format": "double",
                "description": "Precision@K percentage.",
                "type": "number"
              },
              "usersInTest": {
                "format": "int32",
                "description": "The total number of users in the test dataset.",
                "type": "integer"
              },
              "usersConsidered": {
                "format": "int32",
                "description": "A user is only considered if the system recommended at least K items based on the model generated using the training dataset.",
                "type": "integer"
              },
              "usersNotConsidered": {
                "format": "int32",
                "description": "Any users not considered; the users that did not receive at least K recommended items.",
                "type": "integer"
              }
            }
          }
        },
        "error": {
          "description": "Error message to indicate reason in failure cases.",
          "type": "string"
        }
      },
      "description": "Precision metrics for the build based on user's history recommendations."
    },
    "precisionPopularItemRecommend": {
      "type": "object",
      "properties": {
        "precisionMetrics": {
          "description": "Precision metrics that are computed for the test/train dataset.",
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "k": {
                "format": "int32",
                "description": "The value K used to calculate the metric values.",
                "type": "integer"
              },
              "percentage": {
                "format": "double",
                "description": "Precision@K percentage.",
                "type": "number"
              },
              "usersInTest": {
                "format": "int32",
                "description": "The total number of users in the test dataset.",
                "type": "integer"
              },
              "usersConsidered": {
                "format": "int32",
                "description": "A user is only considered if the system recommended at least K items based on the model generated using the training dataset.",
                "type": "integer"
              },
              "usersNotConsidered": {
                "format": "int32",
                "description": "Any users not considered; the users that did not receive at least K recommended items.",
                "type": "integer"
              }
            }
          }
        },
        "error": {
          "description": "Error message to indicate reason in failure cases.",
          "type": "string"
        }
      },
      "description": "Precision metrics for the build based on popular items recommendations."
    },
    "diversityItemRecommend": {
      "type": "object",
      "properties": {
        "percentileBuckets": {
          "description": "Each percentile bucket is represented by a span (min/max values \r\n            that range between 0 and 100). The items close to 100 are the \r\n            most popular items, and the items close to 0 are the least popular.",
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "min": {
                "format": "int32",
                "description": "The beginning percentile of the popularity bucket (inclusive).",
                "type": "integer"
              },
              "max": {
                "format": "int32",
                "description": "The ending percentile of the popularity bucket (exclusive except 100 \r\n            which is inclusive).",
                "type": "integer"
              },
              "percentage": {
                "format": "double",
                "description": "The fraction of all recommended users that belong to the specified popularity bucket.",
                "type": "number"
              }
            }
          }
        },
        "totalItemsRecommended": {
          "format": "int32",
          "description": "The total number of items recommended. (some may be duplicates)",
          "type": "integer"
        },
        "uniqueItemsRecommended": {
          "format": "int32",
          "description": "Number of distinct items that were returned for evaluation.",
          "type": "integer"
        },
        "uniqueItemsInTrainSet": {
          "format": "int32",
          "description": "Number of distinct items in the train dataset.",
          "type": "integer"
        },
        "error": {
          "description": "Error message to indicate reason in failure cases.",
          "type": "string"
        }
      },
      "description": "Diversity metrics for the build based on single item recommendations."
    },
    "diversityUserRecommend": {
      "type": "object",
      "properties": {
        "percentileBuckets": {
          "description": "Each percentile bucket is represented by a span (min/max values \r\n            that range between 0 and 100). The items close to 100 are the \r\n            most popular items, and the items close to 0 are the least popular.",
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "min": {
                "format": "int32",
                "description": "The beginning percentile of the popularity bucket (inclusive).",
                "type": "integer"
              },
              "max": {
                "format": "int32",
                "description": "The ending percentile of the popularity bucket (exclusive except 100 \r\n            which is inclusive).",
                "type": "integer"
              },
              "percentage": {
                "format": "double",
                "description": "The fraction of all recommended users that belong to the specified popularity bucket.",
                "type": "number"
              }
            }
          }
        },
        "totalItemsRecommended": {
          "format": "int32",
          "description": "The total number of items recommended. (some may be duplicates)",
          "type": "integer"
        },
        "uniqueItemsRecommended": {
          "format": "int32",
          "description": "Number of distinct items that were returned for evaluation.",
          "type": "integer"
        },
        "uniqueItemsInTrainSet": {
          "format": "int32",
          "description": "Number of distinct items in the train dataset.",
          "type": "integer"
        },
        "error": {
          "description": "Error message to indicate reason in failure cases.",
          "type": "string"
        }
      },
      "description": "Diversity metrics for the build based on user's history recommendations."
    }
  }
}
{
  "precisionItemRecommend": {
    "precisionMetrics": [
      {
        "k": 0,
        "percentage": 0.0,
        "usersInTest": 0,
        "usersConsidered": 0,
        "usersNotConsidered": 0
      }
    ],
    "error": "string"
  },
  "precisionUserRecommend": {
    "precisionMetrics": [
      {
        "k": 0,
        "percentage": 0.0,
        "usersInTest": 0,
        "usersConsidered": 0,
        "usersNotConsidered": 0
      }
    ],
    "error": "string"
  },
  "precisionPopularItemRecommend": {
    "precisionMetrics": [
      {
        "k": 0,
        "percentage": 0.0,
        "usersInTest": 0,
        "usersConsidered": 0,
        "usersNotConsidered": 0
      }
    ],
    "error": "string"
  },
  "diversityItemRecommend": {
    "percentileBuckets": [
      {
        "min": 0,
        "max": 0,
        "percentage": 0.0
      }
    ],
    "totalItemsRecommended": 0,
    "uniqueItemsRecommended": 0,
    "uniqueItemsInTrainSet": 0,
    "error": "string"
  },
  "diversityUserRecommend": {
    "percentileBuckets": [
      {
        "min": 0,
        "max": 0,
        "percentage": 0.0
      }
    ],
    "totalItemsRecommended": 0,
    "uniqueItemsRecommended": 0,
    "uniqueItemsInTrainSet": 0,
    "error": "string"
  }
}
{
  "type": "object",
  "properties": {
    "precisionItemRecommend": {
      "type": "object",
      "properties": {
        "precisionMetrics": {
          "description": "Precision metrics that are computed for the test/train dataset.",
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "k": {
                "format": "int32",
                "description": "The value K used to calculate the metric values.",
                "type": "integer"
              },
              "percentage": {
                "format": "double",
                "description": "Precision@K percentage.",
                "type": "number"
              },
              "usersInTest": {
                "format": "int32",
                "description": "The total number of users in the test dataset.",
                "type": "integer"
              },
              "usersConsidered": {
                "format": "int32",
                "description": "A user is only considered if the system recommended at least K items based on the model generated using the training dataset.",
                "type": "integer"
              },
              "usersNotConsidered": {
                "format": "int32",
                "description": "Any users not considered; the users that did not receive at least K recommended items.",
                "type": "integer"
              }
            }
          }
        },
        "error": {
          "description": "Error message to indicate reason in failure cases.",
          "type": "string"
        }
      },
      "description": "Precision metrics for the build based on single item recommendations."
    },
    "precisionUserRecommend": {
      "type": "object",
      "properties": {
        "precisionMetrics": {
          "description": "Precision metrics that are computed for the test/train dataset.",
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "k": {
                "format": "int32",
                "description": "The value K used to calculate the metric values.",
                "type": "integer"
              },
              "percentage": {
                "format": "double",
                "description": "Precision@K percentage.",
                "type": "number"
              },
              "usersInTest": {
                "format": "int32",
                "description": "The total number of users in the test dataset.",
                "type": "integer"
              },
              "usersConsidered": {
                "format": "int32",
                "description": "A user is only considered if the system recommended at least K items based on the model generated using the training dataset.",
                "type": "integer"
              },
              "usersNotConsidered": {
                "format": "int32",
                "description": "Any users not considered; the users that did not receive at least K recommended items.",
                "type": "integer"
              }
            }
          }
        },
        "error": {
          "description": "Error message to indicate reason in failure cases.",
          "type": "string"
        }
      },
      "description": "Precision metrics for the build based on user's history recommendations."
    },
    "precisionPopularItemRecommend": {
      "type": "object",
      "properties": {
        "precisionMetrics": {
          "description": "Precision metrics that are computed for the test/train dataset.",
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "k": {
                "format": "int32",
                "description": "The value K used to calculate the metric values.",
                "type": "integer"
              },
              "percentage": {
                "format": "double",
                "description": "Precision@K percentage.",
                "type": "number"
              },
              "usersInTest": {
                "format": "int32",
                "description": "The total number of users in the test dataset.",
                "type": "integer"
              },
              "usersConsidered": {
                "format": "int32",
                "description": "A user is only considered if the system recommended at least K items based on the model generated using the training dataset.",
                "type": "integer"
              },
              "usersNotConsidered": {
                "format": "int32",
                "description": "Any users not considered; the users that did not receive at least K recommended items.",
                "type": "integer"
              }
            }
          }
        },
        "error": {
          "description": "Error message to indicate reason in failure cases.",
          "type": "string"
        }
      },
      "description": "Precision metrics for the build based on popular items recommendations."
    },
    "diversityItemRecommend": {
      "type": "object",
      "properties": {
        "percentileBuckets": {
          "description": "Each percentile bucket is represented by a span (min/max values \r\n            that range between 0 and 100). The items close to 100 are the \r\n            most popular items, and the items close to 0 are the least popular.",
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "min": {
                "format": "int32",
                "description": "The beginning percentile of the popularity bucket (inclusive).",
                "type": "integer"
              },
              "max": {
                "format": "int32",
                "description": "The ending percentile of the popularity bucket (exclusive except 100 \r\n            which is inclusive).",
                "type": "integer"
              },
              "percentage": {
                "format": "double",
                "description": "The fraction of all recommended users that belong to the specified popularity bucket.",
                "type": "number"
              }
            }
          }
        },
        "totalItemsRecommended": {
          "format": "int32",
          "description": "The total number of items recommended. (some may be duplicates)",
          "type": "integer"
        },
        "uniqueItemsRecommended": {
          "format": "int32",
          "description": "Number of distinct items that were returned for evaluation.",
          "type": "integer"
        },
        "uniqueItemsInTrainSet": {
          "format": "int32",
          "description": "Number of distinct items in the train dataset.",
          "type": "integer"
        },
        "error": {
          "description": "Error message to indicate reason in failure cases.",
          "type": "string"
        }
      },
      "description": "Diversity metrics for the build based on single item recommendations."
    },
    "diversityUserRecommend": {
      "type": "object",
      "properties": {
        "percentileBuckets": {
          "description": "Each percentile bucket is represented by a span (min/max values \r\n            that range between 0 and 100). The items close to 100 are the \r\n            most popular items, and the items close to 0 are the least popular.",
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "min": {
                "format": "int32",
                "description": "The beginning percentile of the popularity bucket (inclusive).",
                "type": "integer"
              },
              "max": {
                "format": "int32",
                "description": "The ending percentile of the popularity bucket (exclusive except 100 \r\n            which is inclusive).",
                "type": "integer"
              },
              "percentage": {
                "format": "double",
                "description": "The fraction of all recommended users that belong to the specified popularity bucket.",
                "type": "number"
              }
            }
          }
        },
        "totalItemsRecommended": {
          "format": "int32",
          "description": "The total number of items recommended. (some may be duplicates)",
          "type": "integer"
        },
        "uniqueItemsRecommended": {
          "format": "int32",
          "description": "Number of distinct items that were returned for evaluation.",
          "type": "integer"
        },
        "uniqueItemsInTrainSet": {
          "format": "int32",
          "description": "Number of distinct items in the train dataset.",
          "type": "integer"
        },
        "error": {
          "description": "Error message to indicate reason in failure cases.",
          "type": "string"
        }
      },
      "description": "Diversity metrics for the build based on user's history recommendations."
    }
  }
}
<BuildMetrics>
  <precisionItemRecommend>
    <precisionMetrics>
      <k>0</k>
      <percentage>0</percentage>
      <usersInTest>0</usersInTest>
      <usersConsidered>0</usersConsidered>
      <usersNotConsidered>0</usersNotConsidered>
    </precisionMetrics>
    <error>string</error>
  </precisionItemRecommend>
  <precisionUserRecommend>
    <precisionMetrics>
      <k>0</k>
      <percentage>0</percentage>
      <usersInTest>0</usersInTest>
      <usersConsidered>0</usersConsidered>
      <usersNotConsidered>0</usersNotConsidered>
    </precisionMetrics>
    <error>string</error>
  </precisionUserRecommend>
  <precisionPopularItemRecommend>
    <precisionMetrics>
      <k>0</k>
      <percentage>0</percentage>
      <usersInTest>0</usersInTest>
      <usersConsidered>0</usersConsidered>
      <usersNotConsidered>0</usersNotConsidered>
    </precisionMetrics>
    <error>string</error>
  </precisionPopularItemRecommend>
  <diversityItemRecommend>
    <percentileBuckets>
      <min>0</min>
      <max>0</max>
      <percentage>0</percentage>
    </percentileBuckets>
    <totalItemsRecommended>0</totalItemsRecommended>
    <uniqueItemsRecommended>0</uniqueItemsRecommended>
    <uniqueItemsInTrainSet>0</uniqueItemsInTrainSet>
    <error>string</error>
  </diversityItemRecommend>
  <diversityUserRecommend>
    <percentileBuckets>
      <min>0</min>
      <max>0</max>
      <percentage>0</percentage>
    </percentileBuckets>
    <totalItemsRecommended>0</totalItemsRecommended>
    <uniqueItemsRecommended>0</uniqueItemsRecommended>
    <uniqueItemsInTrainSet>0</uniqueItemsInTrainSet>
    <error>string</error>
  </diversityUserRecommend>
</BuildMetrics>
{
  "type": "object",
  "properties": {
    "precisionItemRecommend": {
      "type": "object",
      "properties": {
        "precisionMetrics": {
          "description": "Precision metrics that are computed for the test/train dataset.",
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "k": {
                "format": "int32",
                "description": "The value K used to calculate the metric values.",
                "type": "integer"
              },
              "percentage": {
                "format": "double",
                "description": "Precision@K percentage.",
                "type": "number"
              },
              "usersInTest": {
                "format": "int32",
                "description": "The total number of users in the test dataset.",
                "type": "integer"
              },
              "usersConsidered": {
                "format": "int32",
                "description": "A user is only considered if the system recommended at least K items based on the model generated using the training dataset.",
                "type": "integer"
              },
              "usersNotConsidered": {
                "format": "int32",
                "description": "Any users not considered; the users that did not receive at least K recommended items.",
                "type": "integer"
              }
            }
          }
        },
        "error": {
          "description": "Error message to indicate reason in failure cases.",
          "type": "string"
        }
      },
      "description": "Precision metrics for the build based on single item recommendations."
    },
    "precisionUserRecommend": {
      "type": "object",
      "properties": {
        "precisionMetrics": {
          "description": "Precision metrics that are computed for the test/train dataset.",
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "k": {
                "format": "int32",
                "description": "The value K used to calculate the metric values.",
                "type": "integer"
              },
              "percentage": {
                "format": "double",
                "description": "Precision@K percentage.",
                "type": "number"
              },
              "usersInTest": {
                "format": "int32",
                "description": "The total number of users in the test dataset.",
                "type": "integer"
              },
              "usersConsidered": {
                "format": "int32",
                "description": "A user is only considered if the system recommended at least K items based on the model generated using the training dataset.",
                "type": "integer"
              },
              "usersNotConsidered": {
                "format": "int32",
                "description": "Any users not considered; the users that did not receive at least K recommended items.",
                "type": "integer"
              }
            }
          }
        },
        "error": {
          "description": "Error message to indicate reason in failure cases.",
          "type": "string"
        }
      },
      "description": "Precision metrics for the build based on user's history recommendations."
    },
    "precisionPopularItemRecommend": {
      "type": "object",
      "properties": {
        "precisionMetrics": {
          "description": "Precision metrics that are computed for the test/train dataset.",
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "k": {
                "format": "int32",
                "description": "The value K used to calculate the metric values.",
                "type": "integer"
              },
              "percentage": {
                "format": "double",
                "description": "Precision@K percentage.",
                "type": "number"
              },
              "usersInTest": {
                "format": "int32",
                "description": "The total number of users in the test dataset.",
                "type": "integer"
              },
              "usersConsidered": {
                "format": "int32",
                "description": "A user is only considered if the system recommended at least K items based on the model generated using the training dataset.",
                "type": "integer"
              },
              "usersNotConsidered": {
                "format": "int32",
                "description": "Any users not considered; the users that did not receive at least K recommended items.",
                "type": "integer"
              }
            }
          }
        },
        "error": {
          "description": "Error message to indicate reason in failure cases.",
          "type": "string"
        }
      },
      "description": "Precision metrics for the build based on popular items recommendations."
    },
    "diversityItemRecommend": {
      "type": "object",
      "properties": {
        "percentileBuckets": {
          "description": "Each percentile bucket is represented by a span (min/max values \r\n            that range between 0 and 100). The items close to 100 are the \r\n            most popular items, and the items close to 0 are the least popular.",
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "min": {
                "format": "int32",
                "description": "The beginning percentile of the popularity bucket (inclusive).",
                "type": "integer"
              },
              "max": {
                "format": "int32",
                "description": "The ending percentile of the popularity bucket (exclusive except 100 \r\n            which is inclusive).",
                "type": "integer"
              },
              "percentage": {
                "format": "double",
                "description": "The fraction of all recommended users that belong to the specified popularity bucket.",
                "type": "number"
              }
            }
          }
        },
        "totalItemsRecommended": {
          "format": "int32",
          "description": "The total number of items recommended. (some may be duplicates)",
          "type": "integer"
        },
        "uniqueItemsRecommended": {
          "format": "int32",
          "description": "Number of distinct items that were returned for evaluation.",
          "type": "integer"
        },
        "uniqueItemsInTrainSet": {
          "format": "int32",
          "description": "Number of distinct items in the train dataset.",
          "type": "integer"
        },
        "error": {
          "description": "Error message to indicate reason in failure cases.",
          "type": "string"
        }
      },
      "description": "Diversity metrics for the build based on single item recommendations."
    },
    "diversityUserRecommend": {
      "type": "object",
      "properties": {
        "percentileBuckets": {
          "description": "Each percentile bucket is represented by a span (min/max values \r\n            that range between 0 and 100). The items close to 100 are the \r\n            most popular items, and the items close to 0 are the least popular.",
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "min": {
                "format": "int32",
                "description": "The beginning percentile of the popularity bucket (inclusive).",
                "type": "integer"
              },
              "max": {
                "format": "int32",
                "description": "The ending percentile of the popularity bucket (exclusive except 100 \r\n            which is inclusive).",
                "type": "integer"
              },
              "percentage": {
                "format": "double",
                "description": "The fraction of all recommended users that belong to the specified popularity bucket.",
                "type": "number"
              }
            }
          }
        },
        "totalItemsRecommended": {
          "format": "int32",
          "description": "The total number of items recommended. (some may be duplicates)",
          "type": "integer"
        },
        "uniqueItemsRecommended": {
          "format": "int32",
          "description": "Number of distinct items that were returned for evaluation.",
          "type": "integer"
        },
        "uniqueItemsInTrainSet": {
          "format": "int32",
          "description": "Number of distinct items in the train dataset.",
          "type": "integer"
        },
        "error": {
          "description": "Error message to indicate reason in failure cases.",
          "type": "string"
        }
      },
      "description": "Diversity metrics for the build based on user's history recommendations."
    }
  }
}
<BuildMetrics>
  <precisionItemRecommend>
    <precisionMetrics>
      <k>0</k>
      <percentage>0</percentage>
      <usersInTest>0</usersInTest>
      <usersConsidered>0</usersConsidered>
      <usersNotConsidered>0</usersNotConsidered>
    </precisionMetrics>
    <error>string</error>
  </precisionItemRecommend>
  <precisionUserRecommend>
    <precisionMetrics>
      <k>0</k>
      <percentage>0</percentage>
      <usersInTest>0</usersInTest>
      <usersConsidered>0</usersConsidered>
      <usersNotConsidered>0</usersNotConsidered>
    </precisionMetrics>
    <error>string</error>
  </precisionUserRecommend>
  <precisionPopularItemRecommend>
    <precisionMetrics>
      <k>0</k>
      <percentage>0</percentage>
      <usersInTest>0</usersInTest>
      <usersConsidered>0</usersConsidered>
      <usersNotConsidered>0</usersNotConsidered>
    </precisionMetrics>
    <error>string</error>
  </precisionPopularItemRecommend>
  <diversityItemRecommend>
    <percentileBuckets>
      <min>0</min>
      <max>0</max>
      <percentage>0</percentage>
    </percentileBuckets>
    <totalItemsRecommended>0</totalItemsRecommended>
    <uniqueItemsRecommended>0</uniqueItemsRecommended>
    <uniqueItemsInTrainSet>0</uniqueItemsInTrainSet>
    <error>string</error>
  </diversityItemRecommend>
  <diversityUserRecommend>
    <percentileBuckets>
      <min>0</min>
      <max>0</max>
      <percentage>0</percentage>
    </percentileBuckets>
    <totalItemsRecommended>0</totalItemsRecommended>
    <uniqueItemsRecommended>0</uniqueItemsRecommended>
    <uniqueItemsInTrainSet>0</uniqueItemsInTrainSet>
    <error>string</error>
  </diversityUserRecommend>
</BuildMetrics>
{
  "type": "object",
  "properties": {
    "precisionItemRecommend": {
      "type": "object",
      "properties": {
        "precisionMetrics": {
          "description": "Precision metrics that are computed for the test/train dataset.",
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "k": {
                "format": "int32",
                "description": "The value K used to calculate the metric values.",
                "type": "integer"
              },
              "percentage": {
                "format": "double",
                "description": "Precision@K percentage.",
                "type": "number"
              },
              "usersInTest": {
                "format": "int32",
                "description": "The total number of users in the test dataset.",
                "type": "integer"
              },
              "usersConsidered": {
                "format": "int32",
                "description": "A user is only considered if the system recommended at least K items based on the model generated using the training dataset.",
                "type": "integer"
              },
              "usersNotConsidered": {
                "format": "int32",
                "description": "Any users not considered; the users that did not receive at least K recommended items.",
                "type": "integer"
              }
            }
          }
        },
        "error": {
          "description": "Error message to indicate reason in failure cases.",
          "type": "string"
        }
      },
      "description": "Precision metrics for the build based on single item recommendations."
    },
    "precisionUserRecommend": {
      "type": "object",
      "properties": {
        "precisionMetrics": {
          "description": "Precision metrics that are computed for the test/train dataset.",
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "k": {
                "format": "int32",
                "description": "The value K used to calculate the metric values.",
                "type": "integer"
              },
              "percentage": {
                "format": "double",
                "description": "Precision@K percentage.",
                "type": "number"
              },
              "usersInTest": {
                "format": "int32",
                "description": "The total number of users in the test dataset.",
                "type": "integer"
              },
              "usersConsidered": {
                "format": "int32",
                "description": "A user is only considered if the system recommended at least K items based on the model generated using the training dataset.",
                "type": "integer"
              },
              "usersNotConsidered": {
                "format": "int32",
                "description": "Any users not considered; the users that did not receive at least K recommended items.",
                "type": "integer"
              }
            }
          }
        },
        "error": {
          "description": "Error message to indicate reason in failure cases.",
          "type": "string"
        }
      },
      "description": "Precision metrics for the build based on user's history recommendations."
    },
    "precisionPopularItemRecommend": {
      "type": "object",
      "properties": {
        "precisionMetrics": {
          "description": "Precision metrics that are computed for the test/train dataset.",
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "k": {
                "format": "int32",
                "description": "The value K used to calculate the metric values.",
                "type": "integer"
              },
              "percentage": {
                "format": "double",
                "description": "Precision@K percentage.",
                "type": "number"
              },
              "usersInTest": {
                "format": "int32",
                "description": "The total number of users in the test dataset.",
                "type": "integer"
              },
              "usersConsidered": {
                "format": "int32",
                "description": "A user is only considered if the system recommended at least K items based on the model generated using the training dataset.",
                "type": "integer"
              },
              "usersNotConsidered": {
                "format": "int32",
                "description": "Any users not considered; the users that did not receive at least K recommended items.",
                "type": "integer"
              }
            }
          }
        },
        "error": {
          "description": "Error message to indicate reason in failure cases.",
          "type": "string"
        }
      },
      "description": "Precision metrics for the build based on popular items recommendations."
    },
    "diversityItemRecommend": {
      "type": "object",
      "properties": {
        "percentileBuckets": {
          "description": "Each percentile bucket is represented by a span (min/max values \r\n            that range between 0 and 100). The items close to 100 are the \r\n            most popular items, and the items close to 0 are the least popular.",
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "min": {
                "format": "int32",
                "description": "The beginning percentile of the popularity bucket (inclusive).",
                "type": "integer"
              },
              "max": {
                "format": "int32",
                "description": "The ending percentile of the popularity bucket (exclusive except 100 \r\n            which is inclusive).",
                "type": "integer"
              },
              "percentage": {
                "format": "double",
                "description": "The fraction of all recommended users that belong to the specified popularity bucket.",
                "type": "number"
              }
            }
          }
        },
        "totalItemsRecommended": {
          "format": "int32",
          "description": "The total number of items recommended. (some may be duplicates)",
          "type": "integer"
        },
        "uniqueItemsRecommended": {
          "format": "int32",
          "description": "Number of distinct items that were returned for evaluation.",
          "type": "integer"
        },
        "uniqueItemsInTrainSet": {
          "format": "int32",
          "description": "Number of distinct items in the train dataset.",
          "type": "integer"
        },
        "error": {
          "description": "Error message to indicate reason in failure cases.",
          "type": "string"
        }
      },
      "description": "Diversity metrics for the build based on single item recommendations."
    },
    "diversityUserRecommend": {
      "type": "object",
      "properties": {
        "percentileBuckets": {
          "description": "Each percentile bucket is represented by a span (min/max values \r\n            that range between 0 and 100). The items close to 100 are the \r\n            most popular items, and the items close to 0 are the least popular.",
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "min": {
                "format": "int32",
                "description": "The beginning percentile of the popularity bucket (inclusive).",
                "type": "integer"
              },
              "max": {
                "format": "int32",
                "description": "The ending percentile of the popularity bucket (exclusive except 100 \r\n            which is inclusive).",
                "type": "integer"
              },
              "percentage": {
                "format": "double",
                "description": "The fraction of all recommended users that belong to the specified popularity bucket.",
                "type": "number"
              }
            }
          }
        },
        "totalItemsRecommended": {
          "format": "int32",
          "description": "The total number of items recommended. (some may be duplicates)",
          "type": "integer"
        },
        "uniqueItemsRecommended": {
          "format": "int32",
          "description": "Number of distinct items that were returned for evaluation.",
          "type": "integer"
        },
        "uniqueItemsInTrainSet": {
          "format": "int32",
          "description": "Number of distinct items in the train dataset.",
          "type": "integer"
        },
        "error": {
          "description": "Error message to indicate reason in failure cases.",
          "type": "string"
        }
      },
      "description": "Diversity metrics for the build based on user's history recommendations."
    }
  }
}

Response 400

Code samples

@ECHO OFF

curl -v -X GET "https://westus.api.cognitive.microsoft.com/recommendations/v4.0/models/{modelId}/builds/{buildId}/metrics"
-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}/metrics?" + 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}/metrics");


            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}/metrics?" + $.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}/metrics";
    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}/metrics');
$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}/metrics?%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}/metrics?%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}/metrics')
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