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Metaheuristic Optimization Algorithms Usage in Recommendation System with User Psychological Portrait Generation

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Advances in Computer Science for Engineering and Education VI (ICCSEEA 2023)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 181))

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Abstract

Today, most Internet services are built using user recommendations: product recommendations (e-commerce), movies (Netflix), mobile applications (App Store, Play Market) and software code (GithubCopilot). The purpose of each of the software solutions is determined primarily by the business, which in turn is determined by the needs of the user. All of the above applications use classic neural network optimization methods for recommender systems development.

In this research classical methods of solving the problem of providing relevant recommendations were considered. The existing methods of building recommender systems were analyzed: classic and personalized models. This research proposes a new software method of creating a recommendation system using metaheuristic optimization algorithms with the use of generated user’s psychological portrait, combination of the OCEAN model and 8 types of activities as hyperparameters, with its implementation in the “Entertainment Planner” mobile application for recreation sphere.

The proposed software method uses as a basis a neuro-collaborative filter with metaheuristic optimization methods, which ensures fast convergence and finding a way out of local traps of the function. Thanks to proposed software method, the result of improving the accuracy of the user’s selection of events by 6–8% when using the full set of metaheuristic optimization algorithms in compared with existing methods. Implementation of the proposed software method is the mobile application, which has been developed using native technologies. The software product is implemented in the product-markets, which provides open access to the technology. Imperative technologies were used to implement a neural network for creating a psychological portrait of the recommendation system’s user.

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Authors and Affiliations

  1. National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, 03056, Ukraine

    Mykhailo Vernik & Liubov Oleshchenko

Authors
  1. Mykhailo Vernik
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  2. Liubov Oleshchenko
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Correspondence to Liubov Oleshchenko .

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Editors and Affiliations

  1. Faculty of Applied Mathematics, National Technical University of Ukraine “Igor Sikorsky Kiev Polytechnic Institute”, Ukraine, Kyiv, Ukraine

    Zhengbing Hu

  2. Faculty of Applied Mathematics, National Technical University of Ukraine “Igor Sikorsky Kiev Polytechnic Institute”, Ukraine, Kyiv, Ukraine

    Ivan Dychka

  3. Halmos College of Arts and Sciences, Nova Southeastern University, Fort Lauderdale, FL, USA

    Matthew He

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Vernik, M., Oleshchenko, L. (2023). Metaheuristic Optimization Algorithms Usage in Recommendation System with User Psychological Portrait Generation. In: Hu, Z., Dychka, I., He, M. (eds) Advances in Computer Science for Engineering and Education VI. ICCSEEA 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 181. Springer, Cham. https://doi.org/10.1007/978-3-031-36118-0_14

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