βPβP: A novel approach to filter out malicious rating profiles from recommender systems

Recommender systems are widely deployed to provide user purchasing suggestion on eCommerce websites. The technology that has been adopted by most recommender systems is collaborative filtering. However, with the open nature of collaborative filtering recommender systems, they suffer significant vulnerabilities from being attacked by malicious raters, who inject profiles consisting of biased ratings.In recent years, several attack detection algorithms have been proposed to handle the issue. Unfortunately, their applications are restricted by various constraints. PCA-based methods while having good performance on paper, still suffer from missing values that plague most user–item matrixes. Classification-based methods require balanced numbers of attacks and normal profiles to train the classifiers. The detector based on SPC (Statistical Process Control) assumes that the rating probability distribution for each item is known in advance. In this research, Beta-Protection (βPβP) is proposed to alleviate the problem without the abovementioned constraints. βPβP grounds its theoretical foundation on Beta distribution for easy computation and has stable performance when experimenting with data derived from the public websites of MovieLens.

► Recommender systems may be injected with malicious profiles.
► Prior works eliminated attacking profiles with classification or PCA based methods.
► They either suffer from the lack of negative cases or cannot cope with sparse data.
► To avoid both issues, the work proposed a method based on beta distributions.
► The experiment shows that the proposed method outperforms the others.