Identifying Attack Models for Securing Cluster-based Recommendation System

Title:Identifying Attack Models for Securing Cluster-based Recommendation System

Volume: 14 Issue: 3

Author(s): Amreen Ahmad*, Tanvir Ahmad and Ishita Tripathi

Affiliation:

• Department of Computer Engineering, Jamia Millia Islamia, New Delhi 25,India

Keywords: Recommendation system, shapley value, attack models, security, collaborative filtering, game theory.

Abstract: The immense growth of information has led to the wide usage of recommender systems for retrieving relevant information. One of the widely used methods for recommendation is collaborative filtering. However, such methods suffer from two problems, scalability and sparsity. In the proposed research, the two issues of collaborative filtering are addressed and a cluster-based recommender system is proposed. For the identification of potential clusters from the underlying network, the Shapley value concept is used, which divides users into different clusters. After that, the recommendation algorithm is performed in every respective cluster. The proposed system recommends an item to a specific user based on the ratings of the item’s different attributes. Thus, it reduces the running time of the overall algorithm, since it avoids the overhead of computation involved when the algorithm is executed over the entire dataset. Besides, the security of the recommender system is one of the major concerns nowadays. Attackers can come in the form of ordinary users and introduce bias in the system to force the system function that is advantageous for them. In this paper, we identify different attack models that could hamper the security of the proposed cluster-based recommender system. The efficiency of the proposed research is validated by conducting experiments on student dataset.

Cite this article as:

Ahmad Amreen *, Ahmad Tanvir and Tripathi Ishita , Identifying Attack Models for Securing Cluster-based Recommendation System, Recent Patents on Engineering 2020; 14 (3) . https://dx.doi.org/10.2174/1872212114666200403091053