@inproceedings{6f30df43aa9644b19fe3a03a8b5a6434,
title = "An empirical study of personal factors and social effects on rating prediction",
abstract = "In social networks, the link between a pair of friends has been reported effective in improving recommendation accuracy. Previous studies mainly based on the assumption that any pair of friends shall have similar interests, via minimizing the gap between user{\textquoteright}s taste and the average (or similar) taste of this user{\textquoteright}s friends to reduce the error of rating prediction. However, these methods ignore the diversity of user{\textquoteright}s taste. In this paper, we focus on learning the diversity of user{\textquoteright}s taste and effects from this user{\textquoteright}s friends in terms of rating behavior. We propose a novel recommendation approach, namely Personal factors with Weighted Social effects Matrix Factorization (PWS), which utilities both user{\textquoteright}s taste and social effects to provide recommendations. Experimental results carried out on 3 datasets, show the effectiveness of the proposed approach.",
keywords = "Personal factors, Rating prediction, Social effects",
author = "Zhijin Wang and Yan Yang and Qinmin Hu and Liang He",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2015 ; Conference date: 19-05-2015 Through 22-05-2015",
year = "2015",
doi = "10.1007/978-3-319-18038-0\_58",
language = "英语",
isbn = "9783319180373",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "747--758",
editor = "Tu-Bao Ho and Hiroshi Motoda and Hiroshi Motoda and Ee-Peng Lim and Tru Cao and David Cheung and Zhi-Hua Zhou",
booktitle = "Advances in Knowledge Discovery and Data Mining - 19th Pacific-Asia Conference, PAKDD 2015, Proceedings",
address = "德国",
}