Abstract
The authors present a hybrid model of a recommender system. The system includes the characteristics of collaborative and content filtering. Also, the article describes a population filtering algorithm and the architecture of a recommendation system based on it. The results of experimental studies on an array of benchmarks and an estimation of filtering efficiency based on a hybrid model and a population algorithm are presented. The results are compared with the traditional method of collaborative filtering using the Pearson correlation coefficient.
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The reported study was funded by RFBR according to the research project â„– 18-29-22019 and project â„– 19-07-00570.
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Rodzin, S., Rodzina, O., Rodzina, L. (2020). Bio-inspired Collaborative and Content Filtering Method for Online Recommendation Assistant Systems. In: Silhavy, R. (eds) Artificial Intelligence and Bioinspired Computational Methods. CSOC 2020. Advances in Intelligent Systems and Computing, vol 1225. Springer, Cham. https://doi.org/10.1007/978-3-030-51971-1_9
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DOI: https://doi.org/10.1007/978-3-030-51971-1_9
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