Session-based Recommendations with Recurrent Neural Networks

21 Nov 2015  ·  Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, Domonkos Tikk ·

We apply recurrent neural networks (RNN) on a new domain, namely recommender systems. Real-life recommender systems often face the problem of having to base recommendations only on short session-based data (e.g. a small sportsware website) instead of long user histories (as in the case of Netflix). In this situation the frequently praised matrix factorization approaches are not accurate. This problem is usually overcome in practice by resorting to item-to-item recommendations, i.e. recommending similar items. We argue that by modeling the whole session, more accurate recommendations can be provided. We therefore propose an RNN-based approach for session-based recommendations. Our approach also considers practical aspects of the task and introduces several modifications to classic RNNs such as a ranking loss function that make it more viable for this specific problem. Experimental results on two data-sets show marked improvements over widely used approaches.

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Results from Other Papers


Task Dataset Model Metric Name Metric Value Rank Source Paper Compare
Session-Based Recommendations Diginetica GRU4REC MRR@20 8 # 13
Hit@20 29.45 # 13
Session-Based Recommendations yoochoose1/64 GRU4REC MRR@20 22.89 # 11
HR@20 60.64 # 10

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