文献类型：期刊文献

英文题名：Solving the cold-start problem in recommender systems with social tags

作者：Zhang, Zi-Ke[1,2];Liu, Chuang[3,4];Zhang, Yi-Cheng[1,2];Zhou, Tao[1,5]

机构：[1]Univ Elect Sci & Technol China, Web Sci Ctr, Chengdu 610054, Peoples R China;[2]Univ Fribourg, Dept Phys, CH-1700 Fribourg, Switzerland;[3]E China Univ Sci & Technol, Sch Business, Shanghai 200237, Peoples R China;[4]E China Univ Sci & Technol, Engn Res Ctr Proc Syst Engn, Minist Educ, Shanghai 200237, Peoples R China;[5]Univ Sci & Technol China, Dept Modern Phys, Hefei 230026, Peoples R China

年份：2010

卷号：92

期号：2

外文期刊名：EPL

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000284470600032)】；

基金：This work is partially supported by the Swiss National Science Foundation (Project 200020-121848). Z-KZ and TZ acknowledge the National Natural Science Foundation of China under Grant Nos. 60973069 and 10635040.

语种：英文

摘要：Based on the user-tag-object tripartite graphs, we propose a recommendation algorithm that makes use of social tags. Besides its low cost of computational time, the experimental results on two real-world data sets, Del.icio.us and MovieLens, show that it can enhance the algorithmic accuracy and diversity. Especially, it provides more personalized recommendation when the assigned tags belong to more diverse topics. The proposed algorithm is particularly effective for small-degree objects, which reminds us of the well-known cold-start problem in recommender systems. Further empirical study shows that the proposed algorithm can significantly solve this problem in social tagging systems with heterogeneous object degree. Copyright (C) EPLA, 2010