关键词: 深度协同过滤, 长短期记忆网络, 概率矩阵分解, 推荐系统

Abstract:

The recommendation system is one of the most important technologies to help users quickly discover the content they are interested in from massive amounts of data. Sparsity and cold start are the main problems facing recommendation systems. For the sparsity problem, many recommendation algorithms have considered the use of additional auxiliary information, such as reviews, summary, or summaries, to improve prediction accuracy. These algorithms have indeed improved prediction accuracy to some extent. However, most of the existing algorithms are based on the bag-of-words model, and the understanding and utilization of this auxiliary information lacks depth and remains on the surface. This paper proposes a new type of recommendation system algorithm, the Deep Collaborative Filtering（DCF） algorithm. DCF integrates Long Short-Term Memory（LSTM） networks and Probability Matrix Factorization（PMF）. This algorithm can not only learn user characteristics based on user ratings, but also use natural language processing technology to deeply mine auxiliary information and learn very accurate item characteristics. After verification on the real datasets Movielens 100K and 1M, the standard error of the DCF algorithm is reduced by 2.54% and 3.96% respectively compared with the existing algorithms.

Key words: Deep Collaborative Filtering（DCF）, Long Short-Term Memory（LSTM）, Probability Matrix Factorization（PMF）, recommendation system