Abstract
In the literature, collaborative filtering (CF) approach and its variations have been proposed for building recommender systems. In CF, recommendations for a given user are computed based on the ratings of k nearest neighbours. The nearest neighbours of target user are identified by computing the similarity between the product ratings of the target user and the product ratings of every other user. In this paper, we have proposed an improved approach to compute the neighborhood by exploiting the categories of products. In the proposed approach, ratings given by a user are divided into different sub-groups based on the categories of products. We consider that the ratings of each sub-group are given by a virtual user. For a target user, the recommendations of the corresponding virtual user are computed by employing CF. Next, the recommendations of the corresponding virtual users of the target user are combined for recommendation. The experimental results on MovieLens dataset show that the proposed approach improves the performance over the existing CF approach.
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Sharma, M., Reddy, P.K., Kiran, R.U., Ragunathan, T. (2011). Improving the Performance of Recommender System by Exploiting the Categories of Products. In: Kikuchi, S., Madaan, A., Sachdeva, S., Bhalla, S. (eds) Databases in Networked Information Systems. DNIS 2011. Lecture Notes in Computer Science, vol 7108. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25731-5_12
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DOI: https://doi.org/10.1007/978-3-642-25731-5_12
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