Collaborative Filtering Based Spammer Detection and User Reputation Estimation in Rating System
Chaojun Liu, Junyu Niu, Fangjun Liu
Available Online March 2015.
- https://doi.org/10.2991/etmhs-15.2015.23How to use a DOI?
- Rating system; User reputation; Collaborative filtering; Spammer detection
- Online rating systems play vital role in the recommendation systems and deeply influence the following user choice. It’s common occurrence that spammers contaminate the rating systems with malicious rates. However, most of the researchers pay more attention to the accuracy and capability of user preference prediction, rather than the authenticity to rating systems, which provide basis and resource to the recommendation systems. Taking advantage of recommendation algorithm research achievement, we propose a collaborative filtering based spammer detection and user reputation estimation method which perfectly resolves the sparsity problem in huge rating data and promotes poor user preference property of item-based user reputation algorithms. The experiment shows effectiveness of the algorithm both in traditional mode of spammer attack and newly proposed one in this paper, which highly simulates the behavior of real word spammers.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Chaojun Liu AU - Junyu Niu AU - Fangjun Liu PY - 2015/03 DA - 2015/03 TI - Collaborative Filtering Based Spammer Detection and User Reputation Estimation in Rating System BT - 2015 International Conference on Education Technology, Management and Humanities Science (ETMHS 2015) PB - Atlantis Press SP - 100 EP - 104 SN - 2352-5398 UR - https://doi.org/10.2991/etmhs-15.2015.23 DO - https://doi.org/10.2991/etmhs-15.2015.23 ID - Liu2015/03 ER -