Robustness of collaborative recommendation based on association rule mining

ABSTRACT

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 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 a recommendation algorithm based on the data mining technique of association rule mining. Our results show that the Apriori algorithm offers large improvement in stability and robustness compared to k-nearest neighbor and other model-based techniques we have studied. Furthermore, our results show that Apriori can achieve comparable recommendation accuracy to k-nn.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining

Additional Classification:
  K. Computing Milieux
  K.6 MANAGEMENT OF COMPUTING AND INFORMATION SYSTEMS
      K.6.5 Security and Protection (D.4.6, K.4.2)

General Terms:
Algorithms, Human Factors, Security

Keywords:
association rule mining, collaborative filtering, data mining, recommender systems, security