Abstract
In the context of recommender systems, users may have particular tastes and very unusual preferences comparing to the others. These users are called Grey Sheep Users. It is difficult to find similar users and relevant recommendations for such kind of users. In this work, we deal with trust values in learning users’ behaviours and relations between each other. A trust-based clustering approach is proposed for identifying Grey Sheep Users. The obtained cluster is then exploited to make recommendations for target unusual users.
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Arthur, D., Vassilvitskii, S..: k-means++: The advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035. Society for Industrial and Applied Mathematics (2007)
Barnett, V., Lewis, T.: Outliers in Statistical Data. Wiley, New York (1994)
Castagnos, S., Brun, A., Boyer, A.: When diversity is needed... but not expected! (2013)
Ghazanfar, M.A., Prugel-Bennett, A.: Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems. Expert Syst. Appl. 41(7), 3261–3275 (2014)
Golbeck, J.A.: Computing and applying trust in web-based social networks. Ph.D. thesis (2005)
Gras, B., Brun, A., Boyer, A.: Identifying grey sheep users in collaborative filtering: a distribution-based technique. In: Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization, pp. 17–26. ACM (2016)
Gras, B., Brun, A., Boyer, A.: Can matrix factorization improve the accuracy of recommendations provided to grey sheep users? (2017)
Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recogn. Lett. 31(8), 651–666 (2010)
Lopez-Nores, M., Blanco-Fernandez, Y., Pazos-Arias, J.J., Gil-Solla, A.: Property-based collaborative filtering for health-aware recommender systems. Expert Syst. Appl. 39(8), 7451–7457 (2012)
Lu, C.T., Chen, D., Kou, Y.: Algorithms for spatial outlier detection. In: Third IEEE International Conference on Data Mining, pp. 597–600. IEEE (2003)
Massa, P., Avesani, P.: Trust-aware recommender systems. In: Proceedings of the 2007 ACM Conference on Recommender Systems, pp. 17–24. ACM (2007)
Merialdo, A.K.B.: Clustering for collaborative filtering applications. Intell. Image Process. Data Anal. Inf. Retrieva l3, 199 (1999)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, pp. 175–186. ACM (1994)
Zheng, Y., Agnani, M., Singh, M.: Identification of grey sheep users by histogram intersection in recommender systems. In: Cong, G., Peng, W.-C., Zhang, W.E., Li, C., Sun, A. (eds.) ADMA 2017. LNCS (LNAI), vol. 10604, pp. 148–161. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69179-4_11
Zheng, Y., Agnani, M., Singh, M.: Identifying grey sheep users by the distribution of user similarities in collaborative filtering. In: Proceedings of the 6th Annual Conference on Research in Information Technology, pp. 1–6. ACM (2017)
Sherchan, W., Nepal, S., Paris, C.: A Survey of Trust in Social Networks. IBM Research- Australia, CSIRO ICT Centre
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Bejaoui, G., Ayachi, R. (2020). A Trust-Based Clustering Approach for Identifying Grey Sheep Users. In: Bach Tobji, M.A., Jallouli, R., Samet, A., Touzani, M., Strat, V.A., Pocatilu, P. (eds) Digital Economy. Emerging Technologies and Business Innovation. ICDEc 2020. Lecture Notes in Business Information Processing, vol 395. Springer, Cham. https://doi.org/10.1007/978-3-030-64642-4_7
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