A Robust Collaborative Filtering Approach Based on User Relationships for Recommendation Systems

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

Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports