Gaussian Process Factorization Machines for context-aware recommendations

Context-aware recommendation (CAR) can lead to significant improvements in the relevance of the recommended items by modeling the nuanced ways in which context influences preferences. The dominant approach in context-aware recommendation has been the multidimensional latent factors approach in which users, items, and context variables are represented as latent features in a low-dimensional space. An interaction between a user, item, and a context variable is typically modeled as some linear combination of their latent features. However, given the many possible types of interactions between user, items and contextual variables, it may seem unrealistic to restrict the interactions among them to linearity. To address this limitation, we develop a novel and powerful non-linear probabilistic algorithm for context-aware recommendation using Gaussian processes. The method which we call Gaussian Process Factorization Machines (GPFM) is applicable to both the explicit feedback setting (e.g. numerical ratings as in the Netflix dataset) and the implicit feedback setting (i.e. purchases, clicks). We derive stochastic gradient descent optimization to allow scalability of the model. We test GPFM on five different benchmark contextual datasets. Experimental results demonstrate that GPFM outperforms state-of-the-art context-aware recommendation methods.