Features of building recommendation systems based on neural network technology using multithreading

N.O. Komleva, S.L. Zinovatna, V. V. Liubchenko, O.M. Komlevoi


The article is devoted to the creation of a recommendation system for tourists regarding hotels using a neural network based on a multi- layer perceptron. The work uses the mechanism of parallelization of the training sample of the neural network. To check the quality of the provided recommendations, the average absolute and root mean square errors, accuracy and completeness were used. The results of the experiments showed that when analyzing 10 html pages with descriptions of hotels, the metrics of root mean square error and accuracy gave the best results at 500,000 epochs of neural network training when using 8 processors.

Prombles in programming 2022; 3-4: 289-300


recommendation system; neural network; parallel computing; software engineering

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Yadav N. et al. (2020) Diversity in Recommendation System: Cluster Based Approach. Hybrid Intelligent Systems. 1179, pp. 113–122. Avail- able from: https://doi.org/10.1007/978-3-030-49336-3_12. [Accessed 28/07/2022].

Bag, S., Abhijeet, G. & Manoj, K.T. (2019) An integrated recommender system for improved accuracy and aggregate diversity. Computers Industrial Engineering. 130, pp. 187–197. Available from: https://doi.org/https://doi.org/10.1016/j.cie.2019.02.028. [Accessed 28/07/2022].

Balshetwar, S.V., Tuganayat, R.M. & Regulwar, G. (2019) Frame Tone and Sentiment Analysis. International Journal of Computer Sciences and Engineering. 7, pp. 24–40. Available from: https://doi.org/10.26438/ijcse/v7i6.2440 [Accessed 28/07/2022].

Wankhade, M., Rao, A.C.S. & Kulkarni, C. (2022) A survey on sentiment analysis methods, applications, and challenges. Artif Intell Rev. Available from: https://doi.org/10.1007/s10462-022-10144-1 [Accessed 28/07/2022].

Raviya, K. & Vennila, M. (2022) An Approach for Recommender System Based on Multilevel Sentiment Analysis Using Hybrid Deep Learning Models. 8th International Conference on Smart Structures and Systems (ICSSS). pp. 01–06. Available from: https://doi.org/10.1109/IC- SSS54381.2022.9782172 [Accessed 28/07/2022].

Banker, S. (2016) A Brief Review of Sentiment Analysis Methods. International Journal of Information Sciences and Techniques. 6(1/2), pp. 89–95. Available from: https://doi.org/10.5121/ijist.2016.6210 [Accessed 28/07/2022].

Preethi, G. et al. (2017) Application of Deep Learning to Sentiment Analysis for recommender system on cloud. International Conference on Computer, Information and Telecommunication Systems (CITS). pp. 93–97. Available from: https://doi.org/10.1109/CITS.2017.8035341. [Ac- cessed 28/07/2022].

Mishra, R. K., Urolagin, S. & Jothi, J. A. A. (2020) Sentiment Analysis for POI Recommender Systems. Seventh International Conference on Information Technology Trends (ITT). pp. 174–179. Available from: https://doi.org/10.1109/ITT51279.2020.9320885 [Accessed 28/07/2022]

Qader, W., M. Ameen, M., & Ahmed, B. (2019) An Overview of Bag of Words; Importance, Implementation, Applications, and Challenges. pp. 200–204. Available from: https://doi.org/10.1109/IEC47844.2019.8950616 [Accessed 28/07/2022].

Li, S. & Gong, B. (2021) Word embedding and text classification based on deep learning methods. MATEC Web of Conferences PY. 336. Available from: https://doi.org/10.1051/matecconf/202133606022 [Accessed 28/07/2022].

Hu, W. & Huang, F. (2020) Review of Deep Learning Parallelization and Its Application in Spatial Data Mining. 2020 International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE). pp. 110–115. Available from: https://doi.org/10.1109/ ICBAIE49996.2020.00029 [Accessed 28/07/2022].

Jacovi, A, Sar Shalom, O. & Goldberg, Y. (2018) Understanding Convolutional Neural Networks for Text Classification. Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP. pp. 56–65. Available from: https://doi. org/10.18653/v1/W18-5408 [Accessed 28/07/2022].

Leung H. & Haykin S. (1991) The complex backpropagation algorithm. IEEE Transactions on Signal Processing, 39(9), pp. 2101–2104. Available from: https://doi.org/ 10.1109/78.134446 [Accessed 28/07/2022].

DOI: https://doi.org/10.15407/pp2022.03-04.289


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