The Long Tail of Recommender Systems and How to Leverage It

被引:0
作者
Park, Yoon-Joo [1 ]
Tuzhilin, Alexander [1 ]
机构
[1] NYU, Stern Sch Business, New York, NY 10003 USA
来源
RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS | 2008年
关键词
Long Tail; clustering; recommendation; data mining;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The paper studies the Long Tail problem of recommender systems when many items in the Long Tail have only few ratings, thus making it hard to use them in recommender systems. The approach presented in the paper splits the whole itemset into the head and the tail parts and clusters only the tail items. Then recommendations for the tail items are based on the ratings in these clusters and for the head items on the ratings of individual items. If such partition and clustering are done properly, we show that this reduces the recommendation error rates for the tail items, while maintaining reasonable computational performance.
引用
收藏
页码:11 / 18
页数:8
相关论文
共 7 条
  • [1] Anderson C., 2006, LONG TAIL
  • [2] [Anonymous], 2005, Data Mining Pratical Machine Learning Tools and Techniques
  • [3] [Anonymous], 1998, TECHNICAL REPORT WS
  • [4] FLEDER DM, 2008, 0710 NET I
  • [5] Hervas-Drane A, 2007, 0741 NET I
  • [6] SCHEIN A, 2002, P 25 ACM SIGIR C
  • [7] TRUONG KQ, 2007, IEICE T INFORM SYS D, V90