Abstract
Group recommender systems (GRSs) have played an important role in numerous online applications by providing recommendation to the group of users where satisfaction of the entire group is a major concern. In traditional GRSs, the relevance of all the groups and the items is considered equal which does not produce accurate recommendations. In this paper, we propose a formalization of the GRS based on the relevance concept using profile merging scheme where collaborative filtering (CF) is applied on each group profile to generate effective recommendations to the group by considering the ratings of the items, the relevance of the groups and the relevance of the items. Further, our GRS framework provides relevant similarity measures, relevant prediction and recommendation quality measures. The experimental results on the benchmark MovieLens dataset demonstrate the efficacy of our proposed GRS framework.
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References
Adomavicius, G., Kwon, Y.: New recommendation techniques for multicriteria rating systems. IEEE Intell. Syst. 22(3), 48–55 (2007)
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)
Baltrunas, L., Makcinskas, T., Ricci, F.: Group recommendations with rank aggregation and collaborative filtering. In: Proceedings of the Fourth ACM Conference on Recommender Systems (ACM), pp. 119–126 (2010)
Berkovsky, S., Freyne, J.: Group-based recipe recommendations: analysis of data aggregation strategies. In: Proceedings of the Fourth ACM Conference on Recommender Systems (ACM), pp. 111–118 (2010)
Bobadilla, J., Hernando, A., Ortega, F., Gutiéerrez, A.: Collaborative filtering based on significances. Inf. Sci. 185(1), 1–17 (2012)
Bobadilla, J., Ortega, F., Hernando, A., Gutiéerrez, A.: Recommender systems survey. Knowl. Based Syst. 46, 109–132 (2013)
Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User Adap. Inter. 12(4), 331–370 (2002)
Cantador, I., Castells, P.: Group recommender systems: new perspectives in the social web. In: Recommender systems for the social web, Springer, pp. 139–157 (2012)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. (TOIS) 22(1), 5–53 (2004)
Kompan, M., Bielikova, M.: Group recommendations: survey and perspectives. Comput. Inform. 33(2), 446–476 (2014)
Ortega, F., Hurtado, R., Bobadilla, J., Bojorque, R.: Recommendation to groups of users using the singularities concept. IEEE Access 6, 39745–39761 (2018)
Pizzutilo, S., De Carolis, B., Cozzolongo, G., Ambruoso, F.: Group modeling in a public space: methods, techniques, experiences. In: Proceedings of the 5th WSEAS International Conference on Applied Informatics and Communications, World Scientific and Engineering Academy and Society (WSEAS), pp. 175–180 (2005)
Popescu, G., Pu, P.: What’s the best music you have? Designing music recommendation for group enjoyment in groupfun. In: CHI’12 Extended Abstracts on Human Factors in Computing Systems, ACM, pp. 1673–1678 (2012)
Wang, W., Zhang, G., Lu, J.: Member contribution-based group recommender system. Decis. Support Syst. 87, 80–93 (2016)
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Kumar, V., Jain, S., Kant, V. (2020). Recommendation to Group of Users Using the Relevance Concept. In: Agarwal, S., Verma, S., Agrawal, D. (eds) Machine Intelligence and Signal Processing. MISP 2019. Advances in Intelligent Systems and Computing, vol 1085. Springer, Singapore. https://doi.org/10.1007/978-981-15-1366-4_25
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DOI: https://doi.org/10.1007/978-981-15-1366-4_25
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