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Bio-inspired Collaborative and Content Filtering Method for Online Recommendation Assistant Systems

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Artificial Intelligence and Bioinspired Computational Methods (CSOC 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1225))

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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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Acknowledgments

The reported study was funded by RFBR according to the research project № 18-29-22019 and project № 19-07-00570.

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Authors and Affiliations

  1. Southern Federal University, Nekrasovsky lane 44, 347928, Taganrog, Russia

    Sergey Rodzin, Olga Rodzina & Lada Rodzina

Authors
  1. Sergey Rodzin
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  2. Olga Rodzina
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  3. Lada Rodzina
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Corresponding author

Correspondence to Lada Rodzina .

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Editors and Affiliations

  1. Tomas Bata University, Zlín, Czech Republic

    Radek Silhavy

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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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