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

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• 作者：Zhen-ming Yuan ; Chi Huang ; Xiao-yan Sun…
• 关键词：Recommender system ; Collaborative filtering ; Social tagging ; Interest evolution model ; TP393
• 刊名：Frontiers of Information Technology & Electronic Engineering
• 出版年：2015
• 出版时间：July 2015
• 年：2015
• 卷：16
• 期：7
• 页码：532-540
• 全文大小：621 KB
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• 作者单位：Zhen-ming Yuan (1)
  Chi Huang (1)
  Xiao-yan Sun (1)
  Xing-xing Li (1)
  Dong-rong Xu (2)
  
  1. School of Information Science and Engineering, Hangzhou Normal University, Hangzhou, 311121, China
  2. MRI Unit & Epidemiology Division, Psychiatry Department, Columbia University & New York State Psychiatric Institute, New York, 10032, USA
  
• 刊物类别：Computer Science, general; Electrical Engineering; Computer Hardware; Computer Systems Organization
• 刊物主题：Computer Science, general; Electrical Engineering; Computer Hardware; Computer Systems Organization and Communication Networks; Electronics and Microelectronics, Instrumentation; Communications Engine
• 出版者：Zhejiang University Press
• ISSN：2095-9230

文摘

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. Keywords Recommender system Collaborative filtering Social tagging Interest evolution model