Discovering unknown but interesting items on personal social network

Juang Lin Duan, Shashi Prasad, Jen Wei Huang

研究成果: Conference contribution

5 引文 斯高帕斯(Scopus)

摘要

Social networking service has become very popular recently. Many recommendation systems have been proposed to integrate with social networking websites. Traditional recommendation systems focus on providing popular items or items posted by close friends. This strategy causes some problems. Popular items always occupy the recommendation list and they are usually already known by the user. In addition, items recommended by familiar users, who frequently communicate with the target user, may not be interesting. Moreover, interesting items from similar users with lower popularity are ignored. In this paper, we propose an algorithm, UBI, to discover unknown but interesting items. We propose three scores, i.e., Quartile-aided Popularity Score, Social Behavior Score, and User Similarity Score, to model the popularity of items, the familiarity of friends, and the similarity of users respectively in the target user's personal social network. Combining these three scores, the recommendation list containing unknown but interesting items can be generated. Experimental results show that UBI outperforms traditional methods in terms of the percentages of unknown and interesting items in the recommendation list.

原文English
主出版物標題Advances in Knowledge Discovery and Data Mining - 16th Pacific-Asia Conference, PAKDD 2012, Proceedings
頁面145-156
頁數12
版本PART 2
DOIs
出版狀態Published - 2012
事件16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2012 - Kuala Lumpur, Malaysia
持續時間: 2012 五月 292012 六月 1

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
號碼PART 2
7301 LNAI
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Other

Other16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2012
國家/地區Malaysia
城市Kuala Lumpur
期間12-05-2912-06-01

All Science Journal Classification (ASJC) codes

  • 理論電腦科學
  • 電腦科學(全部)

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