Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (16): 45-49.DOI: 10.3778/j.issn.1002-8331.1907-0280

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Recommendation Algorithm for Improving Asymmetric Similarity and Associated Regularization

LIU Chunling, ZHANG Li   

  1. School of Mechanical Engineering and Automation, Wuhan Textile University, Wuhan 430200, China
  • Online:2020-08-15 Published:2020-08-11



  1. 武汉纺织大学 机械工程与自动化学院,武汉 430200


In order to improve the impact of data sparseness on the recommendation effect in the traditional recommendation system, a recommendation algorithm for improving asymmetric similarity and associated regularization is proposed. According to the asymmetry relationship between different users and different projects, an improved correlation calculation formula is proposed for predicting the score. At the same time, due to the difficulty in obtaining the implicit relationship of socialization, this paper uses the traditional similarity to obtain the neighborhood set as the user social relationship, and the regularization is used to constrain the matrix decomposition objective function to alleviate the data sparse problem caused by user information asymmetry. Finally, the algorithm is validated in some real dataset. The experimental results show that the algorithm can predict the actual score more effectively than the mainstream recommendation algorithms.

Key words: recommendation system, matrix decomposition, collaborative filtering, asymmetric similarity, associated regularization



关键词: 推荐算法, 矩阵分解, 协同过滤, 非对称相似度, 关联正则化