Movie Recommendation via BLSTM

  • Song TangEmail author
  • Zhiyong Wu
  • Kang Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10133)


Traditional recommender systems have achieved remarkable success. However, they only consider users’ long-term interests, ignoring the situation when new users don’t have any profile or user delete their tracking information. In order to solve this problem, the session-based recommendations based on Recurrent Neural Networks (RNN) is proposed to make recommendations taking only the behavior of users into account in a period time. The model showed promising improvements over traditional recommendation approaches.

In this paper, We apply bidirectional long short-term memory (BLSTM) on movie recommender systems to deal with the above problems. Experiments on the MovieLens dataset demonstrate relative improvements over previously reported results on the Recall@N metrics respectively and generate more reliable and personalized movie recommendations when compared with the existing methods.


Movie recommendation Recommendation system BLSTM RNN 



This Work is supported by Natural Science Foundation of China (61433008, 61373145, 61572280, U1435216), National Key Research & Development Program of China (2016YFB1000500), National Basic Research (973) Program of China (2014CB340402).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer Science and Technology, Tsinghua National Laboratory for Information Science and Technology (TNLIST)Tsinghua UniversityBeijingChina
  2. 2.Graduate School at ShenzhenTsinghua UniversityShenzhenChina

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