融合用户对项目和属性偏好的协同过滤算法

计算机科学 ›› 2018, Vol. 45 ›› Issue (11A): 412-416.

融合用户对项目和属性偏好的协同过滤算法

王云超, 刘臻

北京师范大学教育学部北京100875

出版日期:2019-02-26 发布日期:2019-02-26
通讯作者: 刘臻(1972-),男,博士,教授级高工,博士生导师,主要研究方向为数字化学习环境与工程、高分辨率图像变化检测方法与应用,E-mail:liuzhen@bnu.edu.cn
作者简介:王云超(1991-),男,硕士生,主要研究方向为机器学习、推荐算法,E-mail:chester91@foxmail.com
基金资助:
本文受国家863科技支撑计划项目(2012AA12AA407),赛尔网络下一代互联网技术创新项目(NGII20170628)资助。

Collaborative Filtering Algorithm Based on User’s Preference for Items and Attributes

WANG Yun-chao, LIU Zhen

Faculty of Education,Beijing Normal University,Beijing 100875,China

Online:2019-02-26 Published:2019-02-26

摘要/Abstract

摘要： 协同过滤推荐算法是目前推荐系统领域中十分常用的方法。余弦相似度和Pearson相关系数是目前协同过滤推荐算法中计算相似度的两种常用算法。为提高协同过滤推荐算法的准确性,对相似度计算问题进行了研究,针对目前常用的余弦相似度和Pearson相关系数这两种相似度计算方法的不足,通过设计和引入调节因子,分别考虑用户在评分习惯和项目选择上的差异性,以对这两种传统的相似度算法进行优化和改进。另外,考虑到用户的偏好往往与项目所具有的属性有关,设计了衡量用户对属性偏好的参数,通过加权的方式将其与改进后的相似度算法进行融合,提出了一种融合用户评分习惯、项目选择差异及属性偏好的协同过滤推荐算法。在MovieLens数据集上进行的实验表明,相比于传统算法,提出的改进算法更为精确,平均绝对误差和均方根误差得到了明显的降低。

关键词: 调节因子, 推荐系统, 协同过滤, 用户相似度, 属性偏好

Abstract: Collaborative filtering algorithm is one of the most successful and useful technologies in recommendation systems.Cosine similarity and Pearson correlation coefficient are two of the most widely used traditional algorithms to calculate the similarity in collaborative filtering algorithm.In order to reduce the error,an improved collaborative filtering recommendation algorithm was proposed in view of the disadvantages of the two traditional similarity algorithms.The two traditional algorithms were improved by importing two parameters,one of them was proposed for considering the rating habits of users,and the other was imported to measure the difference of items chosen by users.User’s preference is related to project attributes,therefore,a parameter was designed to measure it.The new algorithm was constructed by the improved traditional algorithm and user’s preference for attributes.The results of experiment on MovieLens dataset show that the proposed algorithm has lower mean absolute error (MAE) and root mean square error (RMSE),and has better performance by using the two parameters compared with traditionalalgorithms.

Key words: Collaborative filtering, Parameter for adjustment, Preference for attributes, Recommendation system, User similarity

中图分类号:

TP391

王云超, 刘臻. 融合用户对项目和属性偏好的协同过滤算法[J]. 计算机科学, 2018, 45(11A): 412-416. https://doi.org/

WANG Yun-chao, LIU Zhen. Collaborative Filtering Algorithm Based on User’s Preference for Items and Attributes[J]. Computer Science, 2018, 45(11A): 412-416. https://doi.org/

参考文献

[1]SHARDANAND U,MAES P.Social information filtering:algorithms for automating “word of mouth”[C]∥Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.ACM Press/Addison-Wesley Publishing Co.,1995:210-217.
[2]MALTZ D A.Distributing information for collaborative filtering on Usenet net news[OL].http://dspace.mit.edu/bitstream/handle/1721.1/36454/31314097-MIT.pdf.
[3]RESNICK P,IACOVOU N,SUCHAK M,et al.GroupLens:an open architecture for collaborative filtering of netnews[C]∥Proceedings of the 1994 ACM Conference on Computer Supported Cooperative work.ACM,1994:175-186.
[4]BOBADILLA J,HERNANDO A,ORTEGA F,et al.Collaborative filtering based on significances[J].Information Sciences,2012,185(1):1-17.
[5]ORTEGA F,SÁNCHEZ J L,BOBADILLA J,et al.Improving collaborative filtering-based recommender systems results using Pareto dominance[J].Information Sciences,2013,239(4):50-61.
[6]WANG S,ZHAO Z,HONG X.The Research on Collaborative Filtering Recommendation Algorithm Based on Improved Clustering Processing[C]∥IEEE International Conference on Computer and Information Technology;Ubiquitous Computing and Communications;Dependable,Autonomic and Secure Computing.Pervasive Intelligence and Computing.IEEE,2015:1012-1015.
[7]邢春晓,高凤荣,战思南,等.适应用户兴趣变化的协同过滤推荐算法[J].计算机研究与发展,2007,44(2):296-301.
[8]王吉源,黎晨,王婵娟.用户属性加权活跃近邻的协同过滤算法[J].计算机应用研究,2016,33(12):3625-3629.
[9]赵文涛,王春春,成亚飞,等.基于用户多属性与兴趣的协同过滤算法[J].计算机应用研究,2016,33(12):3630-3633.
[10]ADOMAVICIUS G,TUZHILIN A.Toward the Next Generation of Recommender Systems:A Survey of the State-of-the-Art and Possible Extensions[J].IEEE Transactions on Knowledge &Data Engineering,2005,17(6):734-749.
[11]HARPER F M,KONSTAN J A.The MovieLens Datasets:History and Context[J].Acm Transactions on Interactive Intelligent Systems,2015,5(4):19.

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