Collaborative Filtering with Recurrent Neural Networks

Abstract

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.

References in corpus (6)

• Adam: A Method for Stochastic Optimization
• LSTM: A Search Space Odyssey
• ADADELTA: An Adaptive Learning Rate Method
• BPR: Bayesian Personalized Ranking from Implicit Feedback
• Using Temporal Data for Making Recommendations
• An MDP-based Recommender System

Cited by in corpus (5)

• Deep Learning based Recommender System: A Survey and New Perspectives
• Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
• Learning Tree-based Deep Model for Recommender Systems
• Use of Deep Learning in Modern Recommendation System: A Summary of Recent Works
• Leveraging Long and Short-term Information in Content-aware Movie Recommendation