3.11 Recommendations

3.11.1 Recommendation Model Types

3.11.2 Training a Recommendation Model

3.11.3 Using a Recommendation Model

3.11.4 Generating Recommendations for Ratings and Rankings

3.11.5 Generating Item Recommendations for Users

3.11.6 Generating User Recommendations for Items

3.11.7 Generating Recommendations for Similar Items

3.11.8 Generating Recommendations for Similar Users

Recommendation models find patterns in user behavior to recommend new products based on prior behavior and preferences. Common examples include a streaming service recommending movies and shows based on past viewing history, or an online shopping site recommending products based on prior purchases.

The main goal of recommendation models is to recommend either items that a user will like, or recommend users who may like a specific item. HeatWave AutoML includes recommendation models that can recommend the following:

• The rating that a user will give to an item.

• Users who will like an item.

• Items that a user will like.

• Identify similar items.

• Identify similar users.