文献类型：期刊文献

英文题名：Preference Mining Using Neighborhood Rough Set Model on Two Universes

作者：Zeng, Kai

第一作者：曾凯

通信作者：Zeng, K[1]

机构：[1]Guizhou Inst Technol, Fac Informat Engn, 1 Caiguan Rd, Guiyang 550003, Peoples R China

第一机构：贵州理工学院

通信机构：corresponding author), Guizhou Inst Technol, Fac Informat Engn, 1 Caiguan Rd, Guiyang 550003, Peoples R China.|贵州理工学院;

年份：2016

卷号：2016

外文期刊名：COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE

收录：;EI(收录号：20170203241038);Scopus(收录号：2-s2.0-85008949775);WOS:【SCI-EXPANDED(收录号:WOS:000390451200001)】；

语种：英文

摘要：Preference mining plays an important role in e-commerce and video websites for enhancing user satisfaction and loyalty. Some classical methods are not available for the cold-start problem when the user or the item is new. In this paper, we propose a new model, called parametric neighborhood rough set on two universes (NRSTU), to describe the user and item data structures. Furthermore, the neighborhood lower approximation operator is used for defining the preference rules. Then, we provide the means for recommending items to users by using these rules. Finally, we give an experimental example to show the details of NRSTU-based preference mining for cold-start problem. The parameters of the model are also discussed. The experimental results show that the proposed method presents an effective solution for preference mining. In particular, NRSTU improves the recommendation accuracy by about 19% compared to the traditional method.