A Transfer Probabilistic Collective Factorization Model to Handle Sparse Data in Collaborative Filtering

Data Sparsity incurs serious concern in collaborative filtering (CF). This issue is especially critical for newly launched CF applications where observed ratings are too scarce to learn a good model to predict missing values. There could be, however, information from other related domains which are with relatively denser data that can be utilized. This paper proposes a transfer-learning based approach that exploits probabilistic matrix factorization model trained with variational expectation-maximization (VIM) to resolve data sparsity by using information from multiple auxiliary domains. We conduct experiments on several data combination and report significant improvements over state-of-the-art transfer-based models for collaborative filtering. The results also show that our framework is the only solution that can achieve acceptable performance when each user has only one single rating. The code of our model is available at https://github.com/Kublai-Jing/TIC https://github.com/Kublai-Jing/TIC.