Improving the Diversity of User-based Top-N Recommendation by Cloud Model
Recommender system is one of the most effective technologies to deal with information overload, which has been used in a lot of business systems. Historically, many recommender systems take much focus on prediction accuracy. However, despite their pretty accuracy, they may not be useful to users. A user’s preference is full of uncertainty, including randomness and fuzziness. Unfortunately, a fixed Top-N recommendation list certainly can not describe this kinds of uncertainty, which has leaded a decline of user satisfaction. Cloud Model is a powerful tool to describe uncertainty of knowledge. In this paper, we use Cloud Model to present user’s preference and propose a improved user-based Top-N recommendation algorithm. Our experimental evaluation show that our proposed algorithm can improve the diversity of recommendation list compared with the typical userbased collaborative filtering.
collaborative filtering:recommender system diversity:cloud model
Bing Wang Zhaowen Tao Jun Hu
State Key Lab of Software Development Environment Beihang University Beijing,China
国际会议
The 5th International Conference on Computer Science & Education(第五届国际计算机新技术与教育学术研讨会 ICCSE10)
合肥
英文
1656-1660
2010-08-24（万方平台首次上网日期，不代表论文的发表时间）