TY - GEN
T1 - A collaborative filtering algorithm based on rating distribution
AU - Deng, Shuangyi
AU - He, Liang
AU - Xia, Weiwei
PY - 2008
Y1 - 2008
N2 - Collaborative filter algorithms are one of the most successful recommender technologies in the world, and have been widely adopted in E-commerce. However, these approaches always suffer from poor prediction quality problem. We analyzed the rating distribution of dataset, and dig out that most of the users are interested on several specific topics, which show the user's true interest. So we propose a new collaborative filtering algorithm based on rating distribution (BRDCF). Finally, we experimentally evaluate our approach and compare it with classical collaborative filtering methods; the results demonstrate the effectiveness of our approach.
AB - Collaborative filter algorithms are one of the most successful recommender technologies in the world, and have been widely adopted in E-commerce. However, these approaches always suffer from poor prediction quality problem. We analyzed the rating distribution of dataset, and dig out that most of the users are interested on several specific topics, which show the user's true interest. So we propose a new collaborative filtering algorithm based on rating distribution (BRDCF). Finally, we experimentally evaluate our approach and compare it with classical collaborative filtering methods; the results demonstrate the effectiveness of our approach.
UR - https://www.scopus.com/pages/publications/62949183514
U2 - 10.1109/ITME.2008.4744044
DO - 10.1109/ITME.2008.4744044
M3 - 会议稿件
AN - SCOPUS:62949183514
SN - 9781424423262
T3 - Proceedings of 2008 IEEE International Symposium on IT in Medicine and Education, ITME 2008
SP - 1118
EP - 1122
BT - Proceedings of 2008 IEEE International Symposium on IT in Medicine and Education, ITME 2008
T2 - 2008 IEEE International Symposium on IT in Medicine and Education, ITME 2008
Y2 - 12 December 2008 through 14 December 2008
ER -