基于友邻-用户模型的微博主题推荐研究

J4 ›› 2013, Vol. 48 ›› Issue (11): 59-65.

基于友邻-用户模型的微博主题推荐研究

郑建兴,张博锋*,岳晓冬，成泽宇

上海大学计算机工程与科学学院, 上海 200072

收稿日期:2013-09-02 出版日期:2013-11-20 发布日期:2013-11-25
通讯作者: 张博锋(1968-),男,研究员,博士,研究方向为智能人机交互、健康信息技术.Email:bfzhang@shu.edu.cn
作者简介:郑建兴(1985- ),男,博士研究生,研究方向为用户模型、个性化推荐.Email:jxzheng185@163.com
基金资助:
国家自然科学基金资助项目(61103067，61303096)；上海市大学生创新创业资助项目(CXSJ12-602)

Research on themes recommendation in microblogging
scenario based on neighbor-user profile

ZHENG Jian-xing, ZHANG Bo-feng*, YUE Xiao-dong， CHENG Ze-yu

School of Computer Engineering and Science, Shanghai University, Shanghai 200072, China

Received:2013-09-02 Online:2013-11-20 Published:2013-11-25

摘要/Abstract

摘要：

在微博社交网络中,微博文本内容短小,主题覆盖较少,同时主题变化快,用户兴趣更新频繁。已有用户模型不能完全准确刻画微博用户变化的兴趣。友邻集由用户认知度高的群体组成,友邻集的主题兴趣可以全面反映目标用户的多样化兴趣。利用目标用户的友邻集,在本体用户模型上构建微博用户的友邻主题兴趣集,计算更新友邻主题兴趣度,提出友邻-用户模型的实现算法。实验表明,在微博社交网络平台中,友邻-用户模型的微博主题推荐精度要优于传统的用户模型。

关键词: 用户模型;本体;友邻-用户模型;微博

Abstract:

In micro-blogging social network, micro-blog content is short text and covered less themes. In addition, themes in network changed fast and user′s interests updated frequently. Existing user profile can′t accurately depict various interests of user. Group of users with high awareness can form neighbor set of user, whose topic interests can comprehensively reflect diverse interests of target user. A neighbor-user profile implementing algorithm was proposed by building neighbor theme interest set of ontology user profile and calculating update of neighbor theme interest degree of ontology user profile in terms of neighbor set of target user. Experiments show that the accuracy of micro-blogging themes recommendation based on neighbor-user profile performs better than that of traditional individual user profile in the micro-blogging social network platform.

Key words: user profile; ontology; neighbor-user profile; micro-blogging

中图分类号:

TP391

郑建兴,张博锋*,岳晓冬，成泽宇. 基于友邻-用户模型的微博主题推荐研究[J]. J4, 2013, 48(11): 59-65.

ZHENG Jian-xing, ZHANG Bo-feng*, YUE Xiao-dong， CHENG Ze-yu. Research on themes recommendation in microblogging
scenario based on neighbor-user profile[J]. J4, 2013, 48(11): 59-65.

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