Abstract
In this paper we show how several similarity measures can be combined for finding similarity between a pair of users for performing Collaborative Filtering in Recommender Systems. Through aggregation of several measures we find super similar and super dissimilar user pairs and assign a different similarity value for these types of pairs. We also introduce another type of similarity relationship which we call medium similar user pairs and use traditional JMSD for assigning similarity values for them. By experimentation with real data we show that our method for finding similarity by aggregation performs better than each of the similarity metrics. Moreover, as we apply all the traditional metrics in the same setting, we can assess their relative performance.
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Acknowledgment
The first three authors were partially supported by their university. The last author was partially supported by the Russian Foundation for Basic Research grants no. 13-07-00504 and 14-01-93960 and made a contribution within the project “Data mining based on applied ontologies and lattices of closed descriptions” supported by the Basic Research Program of the National Research University Higher School of Economics. We also deeply thank the reviewers and Konstantin Vorontsov for their comments and remarks that helped.
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Sarwar, S.M., Hasan, M., Billal, M., Ignatov, D.I. (2015). Similarity Aggregation for Collaborative Filtering. In: Khachay, M., Konstantinova, N., Panchenko, A., Ignatov, D., Labunets, V. (eds) Analysis of Images, Social Networks and Texts. AIST 2015. Communications in Computer and Information Science, vol 542. Springer, Cham. https://doi.org/10.1007/978-3-319-26123-2_23
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DOI: https://doi.org/10.1007/978-3-319-26123-2_23
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