4.6.5.6 Generating Item Recommendations for Users

This topic describes how to generate recommended items for users.

• For known users and known items, the output includes a list of items that the user will most likely give a high rating and the predicted rating or ranking.

• For a new user, and an explicit feedback model, the prediction is the global top K items that received the average highest ratings.

• For a new user, and an implicit feedback model, the prediction is the global top K items with the highest number of interactions.

• For a user who has tried all known items, the prediction is an empty list because it is not possible to recommend any other items. Set remove_seen to false to repeat existing interactions from the training table.

Before You Begin

Review and complete the following tasks:

• Prepare Data for a Recommendation Model

• Train a Recommendation Model

• Generate Predictions for a Recommendation Model

Recommend Items to Users

When you run ML_PREDICT_TABLE or ML_PREDICT_ROW to generate item recommendations, a default value of three items are recommended. To change this value, set the topk parameter.

1. If not already done, load the model. You can use the session variable for the model that is valid for the duration of the connection. Alternatively, you can use the model handle previously set. For the option to set the user name, you can set it to NULL.

   The following example uses the session variable.

   mysql> CALL sys.ML_MODEL_LOAD(@model, NULL);

   The following example uses the model handle.

   mysql> CALL sys.ML_MODEL_LOAD('recommendation_use_case', NULL);

2. Make predictions for the test dataset by using the ML_PREDICT_TABLE routine.

   mysql> CALL sys.ML_PREDICT_TABLE(table_name, model_handle, output_table_name), [options]);

   Replace table_name, model_handle, and output_table_name with your own values. Add options as needed.

   The following example runs ML_PREDICT_TABLE on the testing dataset previously created and sets the topk parameter to 2, so only two items are recommended.

   mysql> CALL sys.ML_PREDICT_TABLE('recommendation_data.testing_dataset', @model, 'recommendation_data.item_recommendations', JSON_OBJECT('recommend', 'items', 'topk', 2));

   Where:

   • recommendation_data.testing_dataset is the fully qualified name of the input table that contains the data to generate predictions for (database_name.table_name).

   • @model is the session variable for the model handle.

   • recommendation_data.item_recommendations is the fully qualified name of the output table with recommendations (database_name.table_name).

   • JSON_OBJECT('recommend', 'items', 'topk', 2) sets the recommendation task to recommend items to users. A maximum of two items to recommend is set.

3. Query the output table to review the recommended top two items for each user in the output table.

   mysql> SELECT * from item_recommendations;
   +---------+---------+--------+--------------------------------------------------------------------+
   | user_id | item_id | rating | ml_results                                                         |
   +---------+---------+--------+--------------------------------------------------------------------+
   | 1       | 2       |    4.0 | {"predictions": {"item_id": ["20", "18"], "rating": [4.7, 3.48]}}  |
   | 1       | 4       |    7.0 | {"predictions": {"item_id": ["20", "18"], "rating": [4.7, 3.48]}}  |
   | 1       | 6       |    1.5 | {"predictions": {"item_id": ["20", "18"], "rating": [4.7, 3.48]}}  |
   | 1       | 8       |    3.5 | {"predictions": {"item_id": ["20", "18"], "rating": [4.7, 3.48]}}  |
   | 10      | 18      |    1.5 | {"predictions": {"item_id": ["20", "3"], "rating": [4.9, 4.65]}}   |
   | 10      | 2       |    6.5 | {"predictions": {"item_id": ["20", "3"], "rating": [4.9, 4.65]}}   |
   | 10      | 5       |    3.0 | {"predictions": {"item_id": ["20", "3"], "rating": [4.9, 4.65]}}   |
   | 10      | 6       |    5.5 | {"predictions": {"item_id": ["20", "3"], "rating": [4.9, 4.65]}}   |
   | 2       | 1       |    5.0 | {"predictions": {"item_id": ["3", "17"], "rating": [4.65, 3.38]}}  |
   | 2       | 3       |    8.0 | {"predictions": {"item_id": ["3", "17"], "rating": [4.65, 3.38]}}  |
   | 2       | 5       |    2.5 | {"predictions": {"item_id": ["3", "17"], "rating": [4.65, 3.38]}}  |
   | 2       | 7       |    6.5 | {"predictions": {"item_id": ["3", "17"], "rating": [4.65, 3.38]}}  |
   | 3       | 18      |    7.0 | {"predictions": {"item_id": ["20", "3"], "rating": [4.39, 4.17]}}  |
   | 3       | 2       |    3.5 | {"predictions": {"item_id": ["20", "3"], "rating": [4.39, 4.17]}}  |
   | 3       | 5       |    6.5 | {"predictions": {"item_id": ["20", "3"], "rating": [4.39, 4.17]}}  |
   | 3       | 8       |    2.5 | {"predictions": {"item_id": ["20", "3"], "rating": [4.39, 4.17]}}  |
   | 4       | 1       |    5.5 | {"predictions": {"item_id": ["20", "3"], "rating": [5.71, 5.42]}}  |
   | 4       | 3       |    8.5 | {"predictions": {"item_id": ["20", "3"], "rating": [5.71, 5.42]}}  |
   | 4       | 6       |    2.0 | {"predictions": {"item_id": ["20", "3"], "rating": [5.71, 5.42]}}  |
   | 4       | 7       |    5.5 | {"predictions": {"item_id": ["20", "3"], "rating": [5.71, 5.42]}}  |
   | 5       | 12      |    5.0 | {"predictions": {"item_id": ["20", "18"], "rating": [5.05, 3.74]}} |
   | 5       | 2       |    7.0 | {"predictions": {"item_id": ["20", "18"], "rating": [5.05, 3.74]}} |
   | 5       | 4       |    1.5 | {"predictions": {"item_id": ["20", "18"], "rating": [5.05, 3.74]}} |
   | 5       | 6       |    4.0 | {"predictions": {"item_id": ["20", "18"], "rating": [5.05, 3.74]}} |
   | 6       | 3       |    6.0 | {"predictions": {"item_id": ["20", "3"], "rating": [5.25, 4.98]}}  |
   | 6       | 5       |    1.5 | {"predictions": {"item_id": ["20", "3"], "rating": [5.25, 4.98]}}  |
   | 6       | 7       |    4.5 | {"predictions": {"item_id": ["20", "3"], "rating": [5.25, 4.98]}}  |
   | 6       | 8       |    7.0 | {"predictions": {"item_id": ["20", "3"], "rating": [5.25, 4.98]}}  |
   | 7       | 1       |    6.5 | {"predictions": {"item_id": ["20", "3"], "rating": [5.41, 5.13]}}  |
   | 7       | 4       |    3.0 | {"predictions": {"item_id": ["20", "3"], "rating": [5.41, 5.13]}}  |
   | 7       | 5       |    5.5 | {"predictions": {"item_id": ["20", "3"], "rating": [5.41, 5.13]}}  |
   | 7       | 9       |    8.0 | {"predictions": {"item_id": ["20", "3"], "rating": [5.41, 5.13]}}  |
   | 8       | 2       |    8.5 | {"predictions": {"item_id": ["20", "18"], "rating": [4.53, 3.35]}} |
   | 8       | 4       |    2.5 | {"predictions": {"item_id": ["20", "18"], "rating": [4.53, 3.35]}} |
   | 8       | 6       |    5.0 | {"predictions": {"item_id": ["20", "18"], "rating": [4.53, 3.35]}} |
   | 8       | 9       |    3.5 | {"predictions": {"item_id": ["20", "18"], "rating": [4.53, 3.35]}} |
   | 9       | 1       |    5.0 | {"predictions": {"item_id": ["20", "3"], "rating": [5.09, 4.83]}}  |
   | 9       | 3       |    8.0 | {"predictions": {"item_id": ["20", "3"], "rating": [5.09, 4.83]}}  |
   | 9       | 7       |    2.5 | {"predictions": {"item_id": ["20", "3"], "rating": [5.09, 4.83]}}  |
   | 9       | 8       |    5.5 | {"predictions": {"item_id": ["20", "3"], "rating": [5.09, 4.83]}}  |
   +---------+---------+--------+--------------------------------------------------------------------+
   40 rows in set (0.0387 sec)

   Review the recommended items in the ml_results column next to item_id. For example, user 1 is predicted to like items 20 and 18. Review the ratings in the ml_results column to review the expected ratings for each recommended item. For example, user 1 is expected to rate item 20 with a value of 4.7, and item 18 with a value of 3.48.

4. Alternatively, if you do not want to generate an entire table of recommended items, you can run ML_PREDICT_ROW to specify a user to recommend items for.

   mysql> SELECT sys.ML_PREDICT_ROW(input_data, model_handle), [options]);

   Replace input_data and model_handle with your own values. Add options as needed.

   The following example runs ML_PREDICT_ROW and specifies user 1 with a limit of two recommended items.

   mysql> SELECT sys.ML_PREDICT_ROW('{"user_id": "1"}', @model,  JSON_OBJECT('recommend', 'users_to_items', 'topk', 2));
   +--------------------------------------------------------------------------------------------------------+
   | sys.ML_PREDICT_ROW('{"user_id": "1"}', @model,  JSON_OBJECT('recommend', 'users_to_items', 'topk', 2)) |
   +--------------------------------------------------------------------------------------------------------+
   | {"user_id": "1", "ml_results": {"predictions": {"rating": [4.7, 3.48], "item_id": ["20", "18"]}}}      |
   +--------------------------------------------------------------------------------------------------------+
   1 row in set (0.7899 sec)

   The predicted items of 20 and 18 and predicted ratings are the same as the one in the output table previously created.

What's Next

• Learn how to generate different types of recommendations:

  • Generate Predictions for Ratings and Rankings

  • Generate User Recommendations for Items

  • Generate Recommendations for Similar Items

  • Generate Recommendations for Similar Users

• Learn how to Score a Recommendation Model.