Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (7): 24-29.DOI: 10.3778/j.issn.1002-8331.1909-0129

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Collaboration Filtering Recommendation Algorithm Based on User Interest and Ratings Difference

LU Hang, SHI Zhibin, LIU Zhongbao   

  1. 1.Research Institute of Big Data and Network Security, School of Big Data, North University of China, Taiyuan 030051, China
    2.School of Software, North University of China, Taiyuan 030051, China
  • Online:2020-04-01 Published:2020-03-28



  1. 1.中北大学 大数据学院 大数据与网络安全研究所,太原 030051
    2.中北大学 软件学院,太原 030051


Aiming at the inaccuracy of single rating similarity calculation in traditional collaborative filtering algorithm, a collaborative filtering recommendation algorithm based on user interest and the ratings difference is proposed. Firstly, the TF-IDF is integrated into the tag weight calculation. An exponential decay function and a time window are used to capture the change of user interest. Secondly, according to the rating matrix, ratings difference similarity algorithm is defined by considering the difference of ratings, evaluation criteria, user and item influence. Finally, user interest similarity and ratings difference similarity are weighted and fused to obtain the nearest neighbors more accurately, which predicts item rating and makes recommendations. Experiments on the Movielens dataset show that the proposed algorithm can effectively improve the recommendation accuracy.

Key words: collaborative filtering, TF-IDF, exponential decay function, time window, difference



关键词: 协同过滤, TF-IDF, 指数衰减函数, 时间窗口, 差异性