The Importance of Similarity Metrics for Representative Users Identification in Recommender Systems

Abstract : In this paper we explore the efficiency of recommendation provided by representative users on behalf of cluster members. Clustering is used to moderate the scalability and diversity issues faced by most recommendation algorithms face. We show through extended evaluation experiments that cluster representative make successful recommendations outperforming the K-nearest neighbor approach which is common in recommender systems that are based on collaborative filtering. However, selection of representative users depends heavily on the similarity metric that is used to identify users with similar preferences. It is shown that the use of different similarity metrics leads, in general, to different representative users while the commonly used Pearson coefficient is the poorest similarity metric in terms of representative user identification.
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Harris Papadopoulos; Andreas S. Andreou; Max Bramer. 6th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI), Oct 2010, Larnaca, Cyprus. Springer, IFIP Advances in Information and Communication Technology, AICT-339, pp.12-21, 2010, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-16239-8_5〉
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Olga Georgiou, Nicolas Tsapatsoulis. The Importance of Similarity Metrics for Representative Users Identification in Recommender Systems. Harris Papadopoulos; Andreas S. Andreou; Max Bramer. 6th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI), Oct 2010, Larnaca, Cyprus. Springer, IFIP Advances in Information and Communication Technology, AICT-339, pp.12-21, 2010, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-16239-8_5〉. 〈hal-01060646〉

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