4.6.5.4 Generating Predictions for a Recommendation Model

After training the model, you can generate predictions. To generate predictions, use the sample data from the testing_dataset dataset. NULL values for any row in the users or items columns generates an error.

Before You Begin

Complete the following tasks:

• Prepare Data for a Recommendation Model

• Train a Recommendation Model

Options for Generating Predictions

The options for ML_PREDICT_ROW and ML_PREDICT_TABLE include the following:

• topk: The number of recommendations to provide. The default is 3.

• recommend: Specifies what to recommend. Permitted values are:

  • ratings: Predicts ratings that users will give. This is the default value.

  • items: Recommends items for users.

  • users: Recommends users for items.

  • users_to_items: This is the same as items.

  • items_to_users: This is the same as users.

  • items_to_items: Recommends similar items for items.

  • users_to_users: Recommends similar users for users.

• remove_seen: If true, the model does not repeat existing interactions from the training table. It only applies to the recommendations items, users, users_to_items, and items_to_users.

What's Next

• Learn about the different ways to generate specific recommendations with a recommendation model:

  • Generate Predictions for Ratings and Rankings.

  • Generate Item Recommendations for Users

  • Generate User Recommendations for Items

  • Generate Recommendations for Similar Items

  • Generate Recommendations for Similar Users