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





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


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.


L.R. Divyaa and N. Pervin. Towards Generating Scalable Personalized Recommendations: Integrating Social Trust, Social Bias, and Geo-spatial Clustering. Decision Support Systems, Elsevier ,2019.

H. Liu, Z. Hu, A. Mian, H. Tian and X. Zhu. A new user similarity model to improve the accuracy of collaborative filtering. Knowledge-Based Systems. Elsevier, 56:156–166, 2014.

M. Sridevi, R.R. Rao and M.V. Rao. A survey on recommender system. International Journal of Computer Science and Information Security. LJS Publishing, 14(5):265, 2016.

R. Pagare, S.A. Patil. Study of collaborative filtering recommendation algorithm-scalability issue. International Journal of Computer Applications. Foundation of Computer Science, 67(25):265, 2013.

A. Jeyasekar, K. Akshay and others. Collaborative Filtering using Euclidean Distance in Recommendation Engine. Indian Journal of Science and Technology. Foundation of Computer Science, 9(37):265, 2016.

R.S. Michalski, R.E. Stepp and E. Diday. A recent advance in data analysis: Clustering objects into classes characterized by conjunctive concepts. Progress in pattern recognition. Elsevier, 33–56, 1981.

V.K. Garg, Y. Narahari and N.N. Narasimha. Novel biobjective clustering (BiGC) based on cooperative game theory. IEEE Transactions on Knowledge and Data Engineering. IEEE, 25(5):1070–1082, 2012.

J. Beel, B. Gipp, S. Langer and M. Genzmehr. Docear: An academic literature suite for searching, organizing and creating academic literature. Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries. ACM, 465–466, 2011.

J.P. Lucas, M. Luz, M. Moreno, R. Anacleto, A.A. Figueiredo and C. Martins. A hybrid recommendation approach for a tourism system. Expert Systems with Applications. Elsevier, 40(9):3532–3550, 2013.

M. Salehi, M. Pourzaferani ans S.A. Razavi. Hybrid attribute-based recommender system for learning material using genetic algorithm and a multidimensional information model. Egyptian Informatics Journal. Elsevier, 14(1):67–78, 2013.

G.R. Xue, C. Lin, Q. Yang, W. Xi, H.J. Zeng, Y. Yu and Z. Chen. Scalable collaborative filtering using cluster-based smoothing. Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 14(1):114–121, 2005.

L. Song, C. Tekin and V.D.S. Mihaela. Clustering based online learning in recommender systems: a bandit approach. 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 4528–4532, 2014.

N.T. Son, D.H. Dat, N.Q. Trung and B.N. Anh. Combination of Dimensionality Reduction and User Clustering for Collaborative-Filtering. Proceedings of the 2017 International Conference on Computer Science and Artificial Intelligence. ACM, 125–130, 2017.

H. Zarzour, Z. Al-Sharif, M. Al-Ayyoub, Y. Jararweh. A new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques. 2018 9th International Conference on Information and Communication Systems (ICICS). IEEE, 102–106, 2018.

U. Gupta and N. Patil. Recommender system based on hierarchical clustering algorithm chameleon. 2015 IEEE International Advance Computing Conference (IACC). IEEE, 1006–1010, 2015.

G. Chalkiadakis, E. Elkind and M. Wooldridge. Computational aspects of cooperative game theory. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 5(6):1–168, 2011.

J.L. Herlocker, J.A. Konstan, L.G. Terveen and J.T. Riedl. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS). ACM, 22(1):5–53, 2004.

F. Cacheda, V. Carneiro, D. Fernández and V. Formoso. Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM Transactions on the Web (TWEB). ACM, 5(1):2, 2011.

N. Mehrbakhsh, O. Ibrahim, K. Bagherifard. A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Systems with Applications. Elsevier, 92:507–520, 2018.

S. Benkessirat, N. Boustia and N. Rezoug. Overview of Recommendation Systems. Smart Education and e-Learning 2019. Springer, 357–372, 2019.

J. Aligon, E. Gallinucci, M. Golfarelli, P. Marcel and S. Rizzi. A collaborative filtering approach for recommending OLAP sessions. Decision Support Systems. Elsevier, 69:20–30, 2015.

R.B. Myerson. Game Theory: Analysis of Conflict, Harvard U. Press, Cambridge, MA. 1991.

M. Tahmasebi, F.F. Ghazvini and M. Esmaeili. Implementation and evaluation of a resource-based learning recommender based on learning style and web page features. Journal of Web Engineering. River Publishers Alsbjergvej 10, Gistrup, 9260, Denmark, 17(3–4):284–304, 2018.

B. Kitchenham. Procedures for performing systematic reviews. Keele, UK, Keele University.33: 1–26, 2004.