Abstract
Nowadays, with the growth of Internet and the uncontrolled increases in amount of data, the user face problems to retrieve the accurate information. To overcome the difficulty of a user, recommender systems are designed that helps the user to find the relevant results. Recommender system is predication-based tools that suggest the items according to behavior of user. This paper is focused on the limitation of gray sheep user problem in collaborating filtering. The gray sheep users are those whose behavior is not similar with any other user. The existence of gray sheep user effects the performance and accuracy of recommender system. To recognize the gray sheep users problem, the clustering techniques can be used. In this paper, we define about various clustering techniques that can be used to categorize the gray sheep users in the system and improve the performance of recommender system.
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Kaur, B., Rani, S. (2021). Identification of Gray Sheep Using Different Clustering Algorithms. In: Goyal, D., Gupta, A.K., Piuri, V., Ganzha, M., Paprzycki, M. (eds) Proceedings of the Second International Conference on Information Management and Machine Intelligence. Lecture Notes in Networks and Systems, vol 166. Springer, Singapore. https://doi.org/10.1007/978-981-15-9689-6_24
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DOI: https://doi.org/10.1007/978-981-15-9689-6_24
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