Kernelized probabilistic matrix factorization for collaborative filtering: exploiting projected user and item graph

Matrix Factorization (MF) techniques have already shown its strong foundation in collaborative filtering (CF), particularly for rating prediction problem. In the basic MF model, the use of additional information such as social network, item tags along with rating has become popular and effective, which results in making the model more complex. However, there are very few studies in recent years, which only use the users rating information for the recommendation. In this paper, we present a new finding on exploiting Projected User and Item Graph in the setting of Kernelized Probabilistic Matrix Factorization (KPMF), which uses different graph kernels from the projected graphs. KPMF works with its latent vector spanning over all users (and items) with Gaussian process priors and tries to capture the covariance structure across users and items from their respective projected graphs. We also explore the ways of building these projected graphs to maximize the prediction accuracy. We implement the model in five real-world datasets and achieve significant performance improvement in terms of RMSE with state-of-the-art MF techniques.