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Fuzzy clustering with optimization for collaborative filtering-based recommender systems

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Abstract

Memory-based collaborative filtering constitutes an important technique of recommender systems mainly due to its simplicity and efficiency. However, it suffers from several fundamentally critical problems when its system makes recommendations based on ratings records of similar users. This study addresses data sparsity and scalability problems that are major drawbacks of the memory-based system. In order to take care of the data sparsity problem, we deduce user interest in movie genres from the user ratings and devise a similarity measure based on the genre preference. Then clusters of users are built based on the genre preference similarity by employing a fuzzy clustering technique, which not only reflects the subjectivity of user ratings but reduces the data scalability problem. Furthermore, we apply an optimization method to the proposed technique to resolve shortcomings of the fuzzy clustering algorithm by using the genetic algorithm. Extensive experiments are conducted to find that the proposed method demonstrates its performance superior or comparable to the previous methods in terms of various metrics. Moreover, the proposed approach turns out to yield the highest prediction accuracy among the experimented methods, thus proving to overcome the serious problem of low prediction encountered with clustering-based collaborative filtering.

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  1. Gyeongin National University of Education, 155 Sammak-ro, Anyang, Republic of Korea

    Soojung Lee

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Lee, S. Fuzzy clustering with optimization for collaborative filtering-based recommender systems. J Ambient Intell Human Comput 13, 4189–4206 (2022). https://doi.org/10.1007/s12652-021-03552-8

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