SemRec: a personalized semantic recommendation method based on weighted heterogeneous information networks

被引:51
|
作者
Shi, Chuan [1 ,2 ]
Zhang, Zhiqiang [3 ]
Ji, Yugang [2 ]
Wang, Weipeng [4 ]
Yu, Philip S. [5 ]
Shi, Zhiping [1 ]
机构
[1] Capital Normal Univ, Beijing Adv Innovat Ctr Imaging Technol, Beijing, Peoples R China
[2] Beijing Univ Posts & Telecommun, Beijing Key Lab Intelligent Telecommun Software &, Beijing Shi, Peoples R China
[3] Ant Financial Serv Grp, Hangzhou, Zhejiang, Peoples R China
[4] Univ Southern Calif, Los Angeles, CA USA
[5] Univ Illinois, Chicago, IL USA
来源
WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS | 2019年 / 22卷 / 01期
基金
中国国家自然科学基金;
关键词
Heterogeneous information network; Recommendation; Similarity; Meta path; FRAMEWORK; GRAPH;
D O I
10.1007/s11280-018-0553-6
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Recently heterogeneous information network (HIN) analysis has attracted a lot of attention, and many data mining tasks have been exploited on HIN. As an important data mining task, recommender system includes a lot of object types (e.g., users, movies, actors, and interest groups in movie recommendation) and the rich relations among object types, which naturally constitute a HIN. The comprehensive information integration and rich semantic information of HIN make it promising to generate better recommendations. However, conventional HINs do not consider the attribute values on links, and the widely used meta path in HIN may fail to accurately capture semantic relations among objects, due to the existence of rating scores (usually ranging from 1 to 5) between users and items in recommender system. In this paper, we introduce the weighted HIN and weighted meta path concepts to subtly depict the path semantics through distinguishing different link attribute values. Furthermore, we propose a semantic path based personalized recommendation method SemRec to predict the rating scores of users on items. Through setting meta paths, SemRec not only flexibly integrates heterogeneous information but also obtains prioritized and personalized weights representing user preferences on paths. Experiments on three real datasets illustrate that SemRec achieves better recommendation performance through flexibly integrating information with the help of weighted meta paths. Moreover, extensive experiments validate the benefits of weighted meta paths.
引用
收藏
页码:153 / 184
页数:32
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