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

• 数据技术 • 上一篇    下一篇

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. 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 出版日期:2014-12-01 发布日期:2014-12-31
  • 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. 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:2014-12-01 Published:2014-12-31
  • Contact: TAO Yongcai

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.

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