4.6.5 Generating Recommendations

4.6.5.1 Recommendation Task Types

4.6.5.2 Preparing Data for a Recommendation Model

4.6.5.3 Training a Recommendation Model

4.6.5.4 Generating Predictions for a Recommendation Model

4.6.5.5 Generating Predictions for Ratings and Rankings

4.6.5.6 Generating Item Recommendations for Users

4.6.5.7 Generating User Recommendations for Items

4.6.5.8 Generating Recommendations for Similar Items

4.6.5.9 Generating Recommendations for Similar Users

4.6.5.10 Scoring a Recommendation Model

Recommendation models find patterns in user behavior to recommend products and users 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. 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.

The following tasks use a dataset generated by OCI GenAI using Meta Llama Models. The recommendation use-case is to create a machine learning model based on users giving a rating of 1 to 10 for different items.

To generate your own datasets to create machine learning models in MySQL AI, learn how to Generate Text-Based Content.

Note

Datasets were generated using Meta Llama models. Your use of this Llama model is subject to your Oracle agreements and this Llama license agreement: https://downloads.mysql.com/docs/LLAMA_31_8B_INSTRUCT-license.pdf.