GPUTENSOR: Efficient tensor factorization for context-aware recommendations

Recommendation systems play an important role in many practical applications that help users manage information and provide personalized recommendations. The context in which a choice is made is an important factor for recommendation systems. Recently, researchers extended the classical matrix factorization to enable generic integration of contextual information by modeling the relevant data as a tensor. However, current tensor factorization methods have two important limitations: (1) computing costs can be very high in practice and (2) they can only be applied to static conditions. In this paper, we propose the GPUTENSOR, a parallel tensor factorization algorithm, to accelerate the tensor factorization of large datasets to support efficient context-aware recommendations. The basic idea of this algorithm is to partition a tensor into smaller blocks and then exploit the inherent parallelism and high memory bandwidth of Graphics Processing Units (GPU) to perform tensor related operations in parallel. Furthermore, based on the observation that contextual information changes dynamically and frequently, we present GPUTENSOR+, an effective incremental method that models the apparent changes of a tensor by adaptively updating its previously factorized components instead of recomputing them on the whole dataset every time data are changed. Experiments indicate that the proposed methods can achieve significant timesavings without a significant loss in accuracy. As an important data mining tool, these methods can be used a potential basis for many other interesting tensor applications, such as clustering, trend detection, social network analysis, and latent concept discovery.