Response Aware Model-Based Collaborative Filtering

Understand

Previous work on recommender systems mainly focus on fitting the ratings provided by users.

• However, the response patterns, i.e., some items are rated while others not, are generally ignored.
• We argue that failing to observe such response patterns can lead to biased parameter estimation and sub-optimal model performance.
• Although several pieces of work have tried to model users' response patterns, they miss the effectiveness and interpretability of the successful matrix factorization collaborative filtering approaches.