Abstract
In recommender systems field (RS), considering a commercial system perspective involves covering in an accurate manner most items available in the market. However, in the memory based collaborative filtering (CF),the recommendation ability is limited because of the huge size of users and items. For this reason, the clustering algorithms were employed to improve the scalability of the system by partitioning users data into clusters then performing computations on each cluster separately. We propose in this paper a recommendation approach that targets two well-known issues: the scalability problem and the recommendation diversity. Our contribution consists of two successive stages: a) K-nearest neighbor (KNN) algorithm based on the use of an adapted similarity measure. b) An adjusted neighborhood selection performed by a genetic algorithm. The approach aims to improve the quality of the neighborhood set by exploring the reduced search space obtained in the first step, to choose among them the best ones who can enhance the quality of the recommendations. The proposed algorithm was compared to baseline recommender systems and showed competitive results in terms of the diversity and the precision of the recommendations.
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Berbague, C., Karabadji, N.E.i., Seridi, H. (2019). Enhancing the Sales Diversity Using a Two-Stage Improved KNN Algorithm. In: Chikhi, S., Amine, A., Chaoui, A., Saidouni, D.E. (eds) Modelling and Implementation of Complex Systems. MISC 2018. Lecture Notes in Networks and Systems, vol 64. Springer, Cham. https://doi.org/10.1007/978-3-030-05481-6_15
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DOI: https://doi.org/10.1007/978-3-030-05481-6_15
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