Recommendation Systems

Build recommendation systems using collaborative filtering and ranking.

Collaborative Filtering

Train collaborative filtering model:

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CREATE TABLE cf_ratings (
    user_id INTEGER,
    item_id INTEGER,
    rating FLOAT4
);

CREATE TEMP TABLE cf_model AS
SELECT train_collaborative_filter('cf_ratings', 'user_id', 'item_id', 'rating') AS model_id;

Generate Recommendations

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SELECT
    user_id,
    item_id,
    predict_collaborative_filter((SELECT model_id FROM cf_model), user_id, item_id) AS predicted_rating
FROM cf_ratings
LIMIT 10;

Learn More

For detailed documentation on recommendation algorithms, evaluation metrics, cold start problems, and hybrid recommendation systems, visit:

Recommendation Systems Documentation

Related Topics

• Vector Search - Similarity-based recommendations
• Quality Metrics - Evaluate recommendation quality