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 ion 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 c...

• Type: Journal
• Year: 2008
• Where: DEBU
• Authors: Jeff J. Sandvig, Bamshad Mobasher, Robin D. Burke
• Keywords: Algorithm, DEBU 2008, Model-based Techniques, Profiles