基于加权动态兴趣度的微博个性化推荐

计算机应用 ›› 2014, Vol. 34 ›› Issue (12): 3491-3496.

基于加权动态兴趣度的微博个性化推荐

陶永才¹,何宗真¹,石磊¹,卫琳²,曹仰杰²

1. 郑州大学信息工程学院,郑州 450001
2. 郑州大学软件技术学院,郑州 450002

收稿日期:2014-07-15 修回日期:2014-08-27 发布日期:2014-12-31 出版日期:2014-12-01
通讯作者: 陶永才
作者简介:陶永才(1975-）,男,河南武陟人,讲师,博士,主要研究方向:高性能计算、社交网络；何宗真(1987-）,女,河南驻马店人,硕士研究生,主要研究方向:社交网络；石磊(1967-）,男,河南郑州人,教授,博士,CCF会员,主要研究方向:高性能计算、社交网络；卫琳(1968-）,女,河南郑州人,副教授,硕士,主要研究方向:Web挖掘；曹仰杰(1976-）,男,河南郑州人,博士,主要研究方向:高性能计算。

Personalized microblogging recommendation based on weighted dynamic degree of interest

TAO Yongcai¹,HE Zongzhen¹,SHI Lei¹,WEI Lin²,CAO Yangjie²

1. School of Information Engineering, Zhengzhou University, Zhengzhou Henan 450001, China;
2. School of Software Technology, Zhengzhou University, Zhengzhou Henan 450002, China

Received:2014-07-15 Revised:2014-08-27 Online:2014-12-31 Published:2014-12-01
Contact: TAO Yongcai

摘要/Abstract

摘要：

针对微博信息量大、用户兴趣随时间变化特征,提出一种基于加权动态兴趣度(WDDI)的微博个性化推荐模型。WDDI模型考虑微博转发特征,并引入时间因子,利用微博主题模型基于转发的狄利克雷分配(RT-LDA)对用户微博进行研究,建立用户对主题的个体动态兴趣模型。通过用户与其关注用户的相似度和交互频率获取用户的群体动态兴趣,将用户个体兴趣与群体兴趣加权结合得到加权动态主题兴趣模型。对用户接收的新微博按动态兴趣度降序排列,实现微博动态个性化推荐。实验表明,WDDI模型较之传统推荐模型,在微博服务中能够更准确地反映用户动态兴趣。

Abstract:

On account of the features that the information in microblogging is enormous and the microbloggers' interests change over time, a personalized microblogging recommendation model based on Weighted Dynamic Degree of Interest (WDDI) was proposed. WDDI model considered the microblogging retweet features and the time factor of tweets, studied the tweets of microbloggers by exploiting the microblog topic model Retweet-Latent Dirichlet Allocation (RT-LDA) and built the individual dynamic interest model. Then WDDI got user's group dynamic interest by the similarity and the interacted frequency between users and their followee. Combining the user's individual interest and the group interest, the weighted dynamic degree of interest model was built. By ranking the new tweets that the user received in descending order by the degree of interest, the dynamic personalized microblogging recommendation was achieved. The experimental results show that WDDI is able to reflect the users' dynamic interest more precisely than the traditional models.

中图分类号:

TP391

陶永才何宗真石磊卫琳曹仰杰. 基于加权动态兴趣度的微博个性化推荐[J]. 计算机应用, 2014, 34(12): 3491-3496.

TAO Yongcai HE Zongzhen SHI Lei WEI Lin CAO Yangjie. Personalized microblogging recommendation based on weighted dynamic degree of interest[J]. Journal of Computer Applications, 2014, 34(12): 3491-3496.

参考文献

［1］KHATER S, ELMONGUI H G, GRACANIN D. Personalized microblogs corpus recommendation based on dynamic users interests［C］// Proceedings of the 2013 International Conference on Social Computing. Piscataway: IEEE, 2013: 979-982.
［2］GAO M, JIN C, QIAN W, et al. Real-time and personalized recommendation on microblogging systems［J］. Journal of Computers, 2014, 37(4): 963-975. (高明,金澈清,钱卫宁,等. 面向微博系统的实时个性化推荐［J］.计算机学报, 2014, 37(4): 963-975.)
［3］SHI L,ZHANG C,WEI L. Introducing active index to the microbloggers ranking［J］.Journal of Chinese Computer Systems, 2012, 33(5): 110-114.(石磊, 张聪, 卫琳. 引入活跃指数的微博用户排名机制［J］. 小型微型计算机系统, 2012, 33(5): 110-114.)
［4］PENG F, QIAN X, MENG H, et al. Research on algorithm of extracting micro-blogs hot topics［C］// Proceedings of the 2011 International Conference on Electronics, Communications and Control. Piscataway: IEEE, 2011: 986-989.
［5］PHELAN O, MCCARTHY K, BENNETT M, et al. Terms of a feather: content-based news recommendation and discovery using twitter［M］. Berlin: Springer, 2011: 448-459.
［6］RICCI F, ROKACH L, SHAPIRA B. Introduction to recommender systems handbook［M］. Berlin: Springer, 2011.
［7］MA W, DENG Y. New feature weight calculation method for short text［J］. Journal of Computer Applications, 2013, 33(8): 2280-2282.(马雯雯, 邓一贵. 新的短文本特征权重计算方法［J］. 计算机应用, 2013, 33(8): 2280-2282.)
［8］STEYVERS M, GRIFFITHS T. Probabilistic topic models［J］. Handbook of Latent Semantic Analysis, 2007, 427(7): 424-440.
［9］BLEI D M, NG A Y, JORDAN M I. Latent Dirichlet allocation［J］. The Journal of Machine Learning Research, 2003,3(5): 993-1022.
［10］HONG L, DAVISON B D. Empirical study of topic modeling in twitter［C］// Proceedings of the 1st Workshop on Social Media Analytics. New York: ACM, 2010: 80-88.
［11］GENCY,SAKAMOTO Y,NICKERSON J V. Discovering context: classifying tweets through a semantic transform based on Wikipedia[M]. Berlin: Springer, 2011: 484-492
［12］RAMAGE D, DUMAIS S T, LIEBLING D J. Characterizing microblogs with topic models[C]// Proceedings of the 2010 International Conference on Weblogs and Social Media (ICWSM). Washington, DC: AAAI Press, 2010, 10: 1-1
［13］YAN Q, WU L, ZHENG L. Social network based microblog user behavior analysis［J］. Physica A: Statistical Mechanics and its Applications, 2013, 392(7): 1712-1723.
［14］LI K, LING X. Personal recommendation based on cloud model and user clustering［J］. Journal of Computer Applications, 2013, 33(10): 2804-2806.(李克潮, 凌霄娥. 云模型与用户聚类的个性化推荐［J］. 计算机应用, 2013, 33(10): 2804-2806.）
［15］WANG C, ZHANG J. Improved K-means algorithm based on latent Dirichlet allocation for text clustering［J］. Journal of Computer Applications, 2014, 34(1): 249-254.(王春龙, 张敬旭. 基于 LDA 的改进 K-means 算法在文本聚类中的应用［J］. 计算机应用, 2014, 34(1): 249-254.）
［16］BLEI D M. Probabilistic topic models［J］. Communications of the ACM, 2012, 55(4): 77-84.
［17］Sina Weibo. Open platform［EB/OL］. ［2014-02-01］. http://open.weibo.com/ wiki/%E5%BE%AE%E5%8D%9AAPI.(新浪微博.开放平台［EB/OL］. ［2014-02-01］. http://open.weibo.com/ wiki/%E5%BE%AE%E5%8D%9AAPI.）
［18］ZHANG C, SUN J, DING Y. Topic mining for microblog based on MB-LDA model［J］.Journal of Computer Research and Development, 2011, 48(10): 1975-1802.(张晨逸, 孙建伶, 丁轶群. 基于 MB-LDA 模型的微博主题挖掘［J］. 计算机研究与发展, 2011, 48(10): 1795-1802.)
［19］ZHU S. Personalized recommendation based on micro-blogging references［D］. Beijing: Beijing University of Posts and Telecommunications, 2013: 39-40. (朱帅.基于微博引用的个性化推荐［D］. 北京: 北京邮电大学, 2013: 39-40.)
［20］XU Z, ZHANG Y, WU Y, et al. Modeling user posting behavior on social media［C］// Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2012: 545-554.
［21］WANG C, BLEI D M. Collaborative topic modeling for recommending scientific articles［C］// Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2011: 448-456.

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