A Robust Collaborative Filtering Approach Based on User Relationships for Recommendation SystemsReportar como inadecuado

A Robust Collaborative Filtering Approach Based on User Relationships for Recommendation Systems - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

Mathematical Problems in Engineering - Volume 2014 2014, Article ID 162521, 8 pages -

Research Article

School of Software Engineering, Chongqing University, Chongqing 400044, China

Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing 400044, China

School of Engineering, University of Portsmouth, Portsmouth PO1 3AH, UK

Received 12 August 2013; Revised 10 December 2013; Accepted 30 December 2013; Published 19 February 2014

Academic Editor: Xing-Gang Yan

Copyright © 2014 Min Gao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Personalized recommendation systems have been widely used as an effective way to deal with information overload. The common approach in the systems, item-based collaborative filtering CF, has been identified to be vulnerable to “Shilling” attack. To improve the robustness of item-based CF, the authors propose a novel CF approach based on the mostly used relationships between users. In the paper, three most commonly used relationships between users are analyzed and applied to construct several user models at first. The DBSCAN clustering is then utilized to select the valid user model in accordance with how the models benefit detecting spam users. The selected model is used to detect spam user group. Finally, a detection-based CF method is proposed for the calculation of item-item similarities and rating prediction, by setting different weights for suspicious spam users and normal users. The experimental results demonstrate that the proposed approach provides a better robustness than the typical item-based kNN k Nearest Neighbor CF approach.

Autor: Min Gao, Bin Ling, Quan Yuan, Qingyu Xiong, and Linda Yang

Fuente: https://www.hindawi.com/


Documentos relacionados