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Time-Ordered Collaborative Filtering for News Recommendation 被引量：5

Time-Ordered Collaborative Filtering for News Recommendation
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摘要 Faced with hundreds of thousands of news articles in the news websites,it is difficult for users to find the news articles they are interested in.Therefore,various news recommender systems were built.In the news recommendation,these news articles read by a user is typically in the form of a time sequence.However,traditional news recommendation algorithms rarely consider the time sequence characteristic of user browsing behaviors.Therefore,the performance of traditional news recommendation algorithms is not good enough in predicting the next news article which a user will read.To solve this problem,this paper proposes a time-ordered collaborative filtering recommendation algorithm(TOCF),which takes the time sequence characteristic of user behaviors into account.Besides,a new method to compute the similarity among different users,named time-dependent similarity,is proposed.To demonstrate the efficiency of our solution,extensive experiments are conducted along with detailed performance analysis. Faced with hundreds of thousands of news articles in the news websites,it is difficult for users to find the news articles they are interested in.Therefore,various news recommender systems were built.In the news recommendation,these news articles read by a user is typically in the form of a time sequence.However,traditional news recommendation algorithms rarely consider the time sequence characteristic of user browsing behaviors.Therefore,the performance of traditional news recommendation algorithms is not good enough in predicting the next news article which a user will read.To solve this problem,this paper proposes a time-ordered collaborative filtering recommendation algorithm（TOCF）,which takes the time sequence characteristic of user behaviors into account.Besides,a new method to compute the similarity among different users,named time-dependent similarity,is proposed.To demonstrate the efficiency of our solution,extensive experiments are conducted along with detailed performance analysis.
作者 XIAO Yingyuan AI Pengqiang Ching-Hsien Hsu WANG Hongya JIAO Xu
机构地区 Tianjin University of Technology Tianjin Key Lab of Intelligence Computing and Novel Software Technology Chung Hua University Donghua University
出处 《China Communications》 SCIE CSCD 2015年第12期53-62,共10页 中国通信（英文版）
基金 supported by the Natural Science Foundation of China(No.61170174, 61370205) Tianjin Training plan of University Innovation Team(No.TD12-5016)
关键词 新闻网站 时间序列 协同过滤 用户浏览行为 有序 推荐算法 序列特征 性能分析 similarity collaborative compute recommendation filtering users hundreds Collaborative Recommendation interested
分类号 TP391.3 [自动化与计算机技术—计算机应用技术]
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参考文献21

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  • 3Xiang, L., Yuan, Q., et al. Temporal recommen- dation on graphs via long- and short-term pref- erence fusion. In Proceedings of KDD '10. ACM, New York, NY, USA, 2010: 723-732.
  • 4Lathia, N., Hailes, S., et al. Temporal diversity in recommender systems. In Proceedings of SIGIR '10. ACM, New York, NY, USA, 2010: 210-217.
  • 5Moerland, M., Hogenboom, F., et al. Seman- tics-based news recommendation with SF-IDF+. In Proceedings of WlMS '13. ACM, New York, NY, USA, 2013, No. 22.
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  • 8Xiao, Y., Ai, R, Wang, H., Hsu, C. H., Li, Y. ENRS: An Effective Recommender System Using Bayesian Model. In Proceedings of DASFAA '15. Springer, Hanoi, Vietnam, 2015:531-535.
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同被引文献12

引证文献5

二级引证文献14

China Communications
2015年 第12期

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