A New Collaborative Filtering Approach Based on Game Theory for Recommendation Systems

Keywords: Recommendation systems, collaborative filtering, cooperative game theory, Shapley Value

Abstract

Recommendation systems can help internet users to find interesting things that match more with their profile. With the development of the digital age, recommendation systems have become indispensable in our lives. On the one hand, most of recommendation systems of the actual generation are based on Collaborative Filtering (CF) and their effectiveness is proved in several real applications. The main objective of this paper is to improve the recommendations provided by collaborative filtering using clustering. Nevertheless, taking into account the intrinsic relationship between users can enhance the recommendations performances. On the other hand, cooperative game theory techniques such as Shapley Value, take into consideration the intrinsic relationship among users when creating communities. With that in mind, we have used SV for the creation of user communities. Indeed, our proposed algorithm preforms into two steps, the first one consists to generate communities user based on Shapley Value, all taking into account the intrinsic properties between users. It applies in the second step a classical collaborative filtering process on each community to provide the Top-N recommendation. Experimental results show that the proposed approach significantly enhances the recommendation compared to the classical collaborative filtering and k-means based collaborative filtering. The cooperative game theory contributes to the improvement of the clustering based CF process because the quality of the users communities obtained is better.

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Author Biographies

Selma Benkessirat, SIIR/LRDSI, Blida1 University, Blida,Algeria,

Selma Benkessirat is a Ph.D. student at the Computer Sciences Department, Saad Dahlab University, Blida, Algeria. She is a member of the Laboratory LRDSI (Laboratory of Research for the Development of Computerized Systems). Her research interests include machine learning, deep learning, recommender systems and game theory.

Narhimene Boustia, SIIR/LRDSI, Blida1 University, Blida,Algeria,

Narhimene Boustia received the PhD degree in computer science from from USTHB Algiers in 2011. She is Professor and searcher at Blida 1 University. Her research focuses on security of information system, access control and knowledge management.

Rezoug Nachida, SIIR/LRDSI, Blida1 University, Blida,Algeria,

Rezoug Nachida is a senior Lecturer in the Computer science department at Saad Dahlab University in Algeria. Her research interests are primarily related to data warehousing solutions, data mining and OLAP system, decision support system, decision making and context-aware recommender system. Dr. Rezoug has co-authored in numerous papers in proceedings of international conferences and journals. She graduated for his ph.D in Computer Sciences from Superior school of Computer science at Algeria in 2016.

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Published
2021-03-16
Section
Articles