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CLC number: TP393

On-line Access: 2015-07-06

Received: 2014-10-30

Revision Accepted: 2015-04-08

Crosschecked: 2015-06-08

Cited: 0

Clicked: 2344

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Zhen-ming Yuan

http://orcid.org/0000-0002-7255-2010

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Frontiers of Information Technology & Electronic Engineering  2015 Vol.16 No.7 P.532-540

10.1631/FITEE.1400368


A microblog recommendation algorithm based on social tagging and a temporal interest evolution model


Author(s):  Zhen-ming Yuan, Chi Huang, Xiao-yan Sun, Xing-xing Li, Dong-rong Xu

Affiliation(s):  School of Information Science and Engineering, Hangzhou Normal University, Hangzhou 311121, China; more

Corresponding email(s):   zmyuan@hznu.edu.cn, mr.hungtat@hznu.edu.cn, sunxy@hznu.edu.cn, lixingxing0209@hznu.edu.cn, dx2103@columbia.edu

Key Words:  Recommender system, Collaborative filtering, Social tagging, Interest evolution model


Zhen-ming Yuan, Chi Huang, Xiao-yan Sun, Xing-xing Li, Dong-rong Xu. A microblog recommendation algorithm based on social tagging and a temporal interest evolution model[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(7): 532-540.

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Abstract: 
Personalized microblog recommendations face challenges of user cold-start problems and the interest evolution of topics. In this paper, we propose a collaborative filtering recommendation algorithm based on a temporal interest evolution model and social tag prediction. Three matrices are first prepared to model the relationship between users, tags, and microblogs. Then the scores of the tags for each microblog are optimized according to the interest evolution model of tags. In addition, to address the user cold-start problem, a social tag prediction algorithm based on community discovery and maximum tag voting is designed to extract candidate tags for users. Finally, the joint probability of a tag for each user is calculated by integrating the Bayes probability on the set of candidate tags, and the top n microblogs with the highest joint probabilities are recommended to the user. Experiments using datasets from the microblog of Sina Weibo showed that our algorithm achieved good recall and precision in terms of both overall and temporal performances. A questionnaire survey proved user satisfaction with recommendation results when the cold-start problem occurred.

The paper presents a hierarchical collaborative filtering recommendation algorithm based on social tagging which considers the evolution of user's interest over time. The topic is interesting and is relevant to practical problems in recommendation systems.

基于社会标签和时间兴趣演变模型的微博推荐算法

目的:微博推荐面临用户冷启动和主题兴趣变化的挑战。研究考虑主题兴趣变化的个性化微博推荐算法,可在一定程度上同时解决用户冷启动问题。
创新点:提出一种基于时间兴趣演变模型和社会标签预测的协同过滤推荐算法。该算法充分利用社会网络和标签热度随时间的演变模型,提高了推荐准确率。
方法:首先,用三个矩阵建模用户、标签和微博之间的关系(图2)。然后根据标签的兴趣演变模型优化每个微博的标签评分(图3)。对于用户冷启动问题,设计一种基于社区发现和最大标签投票算法来预测用户相关的标签。最后,给用户推荐具有最大候选标签集概率的前n个微博(图1)。
结论:在新浪微博数据集上的实验验证了所提算法在获得好的招回率和准确率的同时,可以较好地符合时间演变性能。问卷调查也证明了在冷启动发生时推荐结果的用户满意度。

关键词:推荐系统;协同过滤;社会化标签;兴趣演变模型

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