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

Zhen-ming YUAN,Chi HUANG,Xiao-yan SUN,Xing-xing LI,Dong-rong XU

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 7, Pages 532-540 doi: 10.1631/FITEE.1400368

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 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.

Keywords： Recommender system Collaborative filtering Social tagging Interest evolution model

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Learning natural ordering of tags in domain-specific Q&A sites

Junfang Jia, Guoqiang Li,jiajunfang816@163.com,li.g@sjtu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 2, Pages 141-286 doi: 10.1631/FITEE.1900645

Abstract: is a defining characteristic of Web 2.0. It allows users of social computing systems (e.g., ) to use free terms to annotate content. However, is really a free action? Existing work has shown that users can develop implicit consensus about what tags best describe the content in an online community. However, there has been no work studying the regularities in how users order tags during . In this paper, we focus on the ing of tags in domain-specific Q&A sites. We study tag sequences of millions of questions in four Q&A sites, i.e., CodeProject, SegmentFault, Biostars, and CareerCup. Our results show that users of these Q&A sites can develop implicit consensus about in which order they should assign tags to questions. We study the relationships between tags that can explain the emergence of ing of tags. Our study opens the path to improve existing tag recommendation and Q&A site navigation by leveraging the ing of tags.

Keywords： Question and answering (Q& A) sites Tagging Natural order Skip gram

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A social tag clustering method based on common co-occurrence group similarity

Hui-zong LI,Xue-gang HU,Yao-jin LIN,Wei HE,Jian-han PAN

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 2, Pages 122-134 doi: 10.1631/FITEE.1500187

Abstract:

Social tagging systems are widely applied in Web 2.0.However, many ambiguous and uncontrolled tags produced by social tagging systems not only worsen users

Keywords： Social tagging systems Tag co-occurrence Spectral clustering Group similarity http://dx.doi.org/10.1631