基于全局相似度的社交网络个性化推荐算法

doi:10.19678/j.issn.1000-3428.0048752

计算机工程 ›› 2018, Vol. 44 ›› Issue (10): 204-208. doi: 10.19678/j.issn.1000-3428.0048752

基于全局相似度的社交网络个性化推荐算法

张艺豪¹,李梁²,赵清华¹,马建芬¹,段倩倩¹

1.太原理工大学信息与计算机学院,太原 030024; 2.太原师范学院管理系,山西晋中 030619

收稿日期:2017-09-21 出版日期:2018-10-15 发布日期:2018-10-15
作者简介:张艺豪(1993—),男,硕士研究生,主研方向为个性化推荐系统、模式识别;李梁,讲师;赵清华、马建芬、段倩倩,副教授。
基金资助:
国家自然科学基金(51505324);山西省国际科技合作计划项目(2013-036)。

Personalized Recommendation Algorithm of Social Network Based on Global Similarity

ZHANG Yihao¹,LI Liang²,ZHAO Qinghua¹,MA Jianfen¹,DUAN Qianqian¹

1.School of Information and Computer Science,Taiyuan University of Technology,Taiyuan 030024,China; 2.Management Department,Taiyuan Normal University,Jinzhong,Shanxi 030619,China

Received:2017-09-21 Online:2018-10-15 Published:2018-10-15

摘要/Abstract

摘要： 针对经典社交网络推荐算法忽略项目相似度信息的问题,提出一种改进的社交网络个性化推荐算法。挖掘项目之间的全局项目相似度信息,分析社交网络用户之间信任值的可靠度,并将两者融合在一种模型中,实现对用户的个性化推荐。在Filmtrust数据集与Ciao数据集上的实验结果表明,与PMF、SocialMF和SoRec算法相比,该算法能够提高推荐准确性,降低在冷启动问题上的推荐误差。

关键词: 全局相似度, 个性化推荐, 社交网络, 数据挖掘, 机器学习

Abstract: Aiming at the problem of neglecting project similarity information in classical social network recommendation algorithm,an improved personalized recommendation algorithm for social network is proposed. The global project similarity information between projects is mined,and the reliability of trust values among social network users is analyzed,and the two are combined in a model to realize personalized recommendation to users. Experimental results on Filmtrust datasets and Ciao datasets show that,compared with PMF,SocialMF and SoRec algorithms,this algorithm can improve the accuracy of recommendation and reduce the recommendation error in cold start problem.

Key words: global similarity, personalized recommendation, social network, data mining, machine learning

中图分类号:

TP391

张艺豪,李梁,赵清华,马建芬,段倩倩. 基于全局相似度的社交网络个性化推荐算法[J]. 计算机工程, 2018, 44(10): 204-208.

ZHANG Yihao,LI Liang,ZHAO Qinghua,MA Jianfen,DUAN Qianqian. Personalized Recommendation Algorithm of Social Network Based on Global Similarity[J]. Computer Engineering, 2018, 44(10): 204-208.

http://www.ecice06.com/CN/Y2018/V44/I10/204

参考文献

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