Active Learning in Recommendation Systems with Multi-level User Preferences

Understand

While recommendation systems generally observe user behavior passively, there has been an increased interest in directly querying users to learn their specific preferences.

• In such settings, considering queries at different levels of granularity to optimize user information acquisition is crucial to efficiently providing a good user experience.
• In this work, we study the active learning problem with multi-level user preferences within the collective matrix factorization (CMF) framework.
• CMF jointly captures multi-level user preferences with respect to items and relations between items (e.g., book genre, cuisine type), generally resulting in improved predictions.