Learning to Recommend from Sparse Data via Generative User Feedback

Understand

Traditional collaborative filtering (CF) based recommender systems tend to perform poorly when the user-item interactions/ratings are highly scarce.

• To address this, we propose a learning framework that improves collaborative filtering with a synthetic feedback loop (CF-SFL) to simulate the user feedback.
• The proposed framework consists of a "recommender" and a "virtual user".
• The "recommender" is formulated as a CF model, recommending items according to observed user preference.