A Survey of Collaborative Recommendation and the Robustness of Model-Based Algorithms

The open nature of collaborative recommender systems allows attackers who inject biased profile data to have a significant impact on the recommendations produced. Standard memory-based collaborative filtering algorithms, such as k-nearest neighbor, are quite vulnerable to profile injection attacks. Previous work has shown that some model-based techniques are more robust than standard k-nn. Model abstraction can inhibit certain aspects of an attack, providing an algorithmic approach to minimizing attack effectiveness. In this paper, we examine the robustness of several recommendation algorithms that use different model-based techniques: user clustering, feature reduction, and association rules. In particular, we consider techniques based on k-means and probabilistic latent semantic analysis (pLSA) that compare the profile of an active user to aggregate user clusters, rather than the original profiles. We then consider a recommendation algorithm that uses principal component analysis (PCA) to calculate the similarity between user profiles based on reduced dimensions. Finally, we consider a recommendation algorithm based on the data mining technique of association rule mining using the Apriori algorithm. Our results show that all techniques offer large improvements in stability and robustness compared to standard k-nearest neighbor. In particular, the Apriori algorithm performs extremely well against lowknowledge attacks, but at a cost of reduced coverage, and the PCA algorithm performs extremely well against focused attacks. Furthermore, our results show that all techniques can achieve comparable recommendation accuracy to standard k-nn.