Artificial Intelligence Recommendation:Call the recommendation engine API

API description

POST /api/rec/feed

Request parameters

item_list

If you have a batch of retrieved data to pass to the API, assign it to the item_list parameter. The item_list parameter is an array where each item is a map object. Each item must contain an item_id field, which represents the item ID. All other fields are optional. If an item contains a score field, its value is treated as the retrieval score. Other fields are treated as item properties.

complex_type_features

Contextual features of complex types must be passed as an array. Each element contains the following fields:

Examples:

1. Pass a single value: [{"name":"age", "type":"int", "values":20}, {"name":"sex", "values":"male", "type":"string"}]

2. Pass an array: [{"name":"list_features", "type":"list<int>", "values":[1,2,3]}]

3. Pass a map: [{"name":"map_features", "type":"map<int,int>", "values":{"1":10,"2":20,"3":30}}], or [{"name":"map_features", "type":"map<int,int>", "values":{"1":"10","2":"20","3":"30"}}]

The type parameter supports the following types: int, int64, float, double, string, list<int>, list<int64>, list<float>, list<double>, list<string>, map<int,int>, map<int,int64>, map<int,float>, map<int,double>, map<int,string>, map<string,int>, map<string,int64>, map<string,float>, map<string,double>, map<string,string>, map<int64,int>, map<int64,int64>, map<int64,float>, map<int64,double>, and map<int64,string>.

Note: You can use the complex_type_features and features parameters at the same time to pass contextual information.

Response

The `items` are as follows:

Error codes

Configuration and call examples

This section provides an example of contextual retrieval, where a retrieval list is passed to the API. The following code shows the engine configuration for the home_feed scenario:

{
  "RunMode": "product",
  "ListenConf": {
    "HttpAddr": "",
    "HttpPort": 8000
  },
  "SortNames": {
    "default": ["ItemRankScore"]
  },
  "FilterNames": {
    "default": ["UniqueFilter"]
  },
  "SceneConfs": {
    "home_feed": {
      "default": {
        "RecallNames": ["ContextItemRecall"]
      }
    }
  }
}

The request body is as follows:

{
  "uid":"13579",
  "size":2,
  "scene_id":"home_feed",
  "item_list":[{"item_id":"1111", "score":1},{"item_id":"222", "score":0.95}]
}

API calls

Call the API using a cURL command

To call the engine service from your local machine using a cURL command, first obtain the request URL and token for the service from the Elastic Algorithm Service (EAS) console, as shown in the following figure.

On the service details page, click View Endpoint Information to view the call information.

curl -X 'POST' -v \
'http://12*****.vpc.hangzhou.pai-eas.aliyuncs.com/api/predict/test/api/rec/feed' \
-H 'Authorization:YmRjZThkM2Z*****************TU5Mw==' \
-d '{
"uid":"13579",
"size":2,
"scene_id":"home_feed",
"item_list":[{"item_id":"1111", "score":1},{"item_id":"222", "score":0.95}]
}'

Response data

{
  "code":200,
  "msg":"success",
  "request_id":"e332fe9c-7d99-45a8-a047-bc7ec33d07f6",
  "size":2,
  "experiment_id":"",
  "items":[
    {
      "item_id":"1111",
      "score":0.9991594902203332,
      "retrieve_id":"ContextItemRecall"
    },
    ...
  ]
}

Call the API using the PAI-Rec diagnostic tool

If you deployed the service on the Service Management page in the PAI-Rec console, you can use the diagnostic tool to debug the service, as shown in the following figure.

Select the service and environment. For Routing Information, select /api/rec/feed.

Item Table: If you have an item details table, you can select it here to associate the item details with the returned data.

Request Data: The request parameters from your recent requests are displayed. You can also enter them manually.

As shown in the following figure, select and enter the relevant parameters, and then click Diagnose to obtain the recommendation results.