Factorization models for context-aware recommendations

The field of implicit feedback based recommender algorithms have gained increased interest in the last few years, driven by the need of many practical applications where no explicit feedback is available. The main difficulty of this recommendation task is the lack of information on the negative preferences of the users that may lead to inaccurate recommendations and scalability issues. In this paper, we adopt the use of contextawareness to improve the accuracy of implicit models—a model extension technique that was applied successfully for explicit algorithms. We present a modified version of the iTALS algorithm (coined iTALSx) that uses a different underlying factorization model. We explore the key differences between these approaches and conduct experiments on five data sets to experimentally determine the advantages of the underlying models. We show that iTALSx outperforms the other method on sparser data sets and is able to model complex user–item relations with fewer factors.

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