TY - GEN
T1 - Discovering unknown but interesting items on personal social network
AU - Duan, Juang Lin
AU - Prasad, Shashi
AU - Huang, Jen Wei
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84861453442&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84861453442&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-30220-6_13
DO - 10.1007/978-3-642-30220-6_13
M3 - Conference contribution
AN - SCOPUS:84861453442
SN - 9783642302190
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 145
EP - 156
BT - Advances in Knowledge Discovery and Data Mining - 16th Pacific-Asia Conference, PAKDD 2012, Proceedings
T2 - 16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2012
Y2 - 29 May 2012 through 1 June 2012
ER -