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Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations

, , , and . Proceedings of the 10th ACM Conference on Recommender Systems, page 241--248. New York, NY, USA, ACM, (2016)
DOI: 10.1145/2959100.2959167

Abstract

Real-life recommender systems often face the daunting task of providing recommendations based only on the clicks of a user session. Methods that rely on user profiles -- such as matrix factorization -- perform very poorly in this setting, thus item-to-item recommendations are used most of the time. However the items typically have rich feature representations such as pictures and text descriptions that can be used to model the sessions. Here we investigate how these features can be exploited in Recurrent Neural Network based session models using deep learning. We show that obvious approaches do not leverage these data sources. We thus introdu…(more)

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