  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.
