Movie recommendation via BLSTM

Abstract

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.