@inproceedings{e0fb884307a94ea085ccb0546044f8d8,
title = "Implicit and explicit trust in collaborative filtering",
abstract = "Recommender Systems based on collaborative filtering could provide users with accurate recommendation. However, sometimes due to data sparsity and cold start of the input ratings matrix, this method could not find similar users accurately. In the past, researchers used implicit trust weight instead of the similarity weight to find similar users, to improve the quality of recommendation [17]. And they often ignore the role of explicit trust in the process of finding similar users. Therefore, in this paper, we explore the calculation of implicit trust and explicit trust. Then according to their role in the recommendation system, we propose a method that combined trust and similarity to get a better recommendation. At last, by experimenting on FilmTrust [5] data set which has the explicit trust matrix, the result showed that the method we proposed significantly improve the quality of recommendation, in addition, implicit trust and explicit trust have a positive effect on the quality of the results of recommendation.",
keywords = "Collaborative filtering, Explicit trust, Implicit trust, Recommender system",
author = "Yuanxin Ouyang and Jingshuai Zhang and Weizhu Xie and Wenge Rong and Zhang Xiong",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 9th International Conference on Knowledge Science, Engineering and Management, KSEM 2016 ; Conference date: 05-10-2016 Through 07-10-2016",
year = "2016",
doi = "10.1007/978-3-319-47650-6\_39",
language = "英语",
isbn = "9783319476490",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "489--500",
editor = "Franz Lehner and Nora Fteimi",
booktitle = "Knowledge Science, Engineering and Management - 9th International Conference, KSEM 2016, Proceedings",
address = "德国",
}