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Collaborative Filtering with Recurrent Neural Networks

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arxiv 1608.07400 v2 pith:YZM7A7L3 submitted 2016-08-26 cs.IR cs.LG

Robin Devooght , Hugues Bersini This is my paper · ORCID
classification cs.IRcs.LG
keywords collaborativefilteringcompetitivelstmmethodsnetworksneuralrecurrent
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We show that collaborative filtering can be viewed as a sequence prediction problem, and that given this interpretation, recurrent neural networks offer very competitive approach. In particular we study how the long short-term memory (LSTM) can be applied to collaborative filtering, and how it compares to standard nearest neighbors and matrix factorization methods on movie recommendation. We show that the LSTM is competitive in all aspects, and largely outperforms other methods in terms of item coverage and short term predictions.

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Cited by 1 Pith paper

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  1. Two-Stage Session-based Recommendations with Candidate Rank Embeddings

    cs.IR 2019-08 conditional novelty 6.0 of 10

    A two-stage session-based recommender that adds Candidate Rank Embeddings to a re-ranker improves Recall@20 and MRR@20 over several baselines.

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