Accurate and Diverse Recommendations via Propensity-Weighted Linear Autoencoders

Kazuma Onishi, Katsuhiko Hayashi, Hidetaka Kamigaito

In real-world recommender systems, user-item interactions are Missing Not At Random (MNAR), as interactions with popular items are more frequently observed than those with less pop…