Response Aware Model-Based Collaborative Filtering
This addresses biased recommendations for users in collaborative filtering, but it is incremental as it builds on existing matrix factorization methods.
The paper tackles the problem of biased parameter estimation in recommender systems by modeling user response patterns, resulting in the Response Aware Probabilistic Matrix Factorization (RAPMF) framework that subsumes PMF and shows empirical merits.
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. To bridge the gap, in this paper, we unify explicit response models and PMF to establish the Response Aware Probabilistic Matrix Factorization (RAPMF) framework. We show that RAPMF subsumes PMF as a special case. Empirically we demonstrate the merits of RAPMF from various aspects.