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

Full Text:



Yadav N. et al. (2020) Diversity in Recommendation System: Cluster Based Approach. Hybrid Intelligent Systems. 1179, pp. 113-122. CrossRef

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. CrossRef

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. CrossRef

Wankhade, M., Rao, A.C.S. & Kulkarni, C. (2022) A survey on sentiment analysis methods, applications, and challenges. Artif Intell Rev.CrossRef

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. CrossRef

Banker, S. (2016) A Brief Review of Sentiment Analysis Methods. International Journal of Information Sciences and Techniques. 6(1/2), pp. 89-95. CrossRef

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. CrossRef

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. CrossRef

Qader, W., M. Ameen, M., & Ahmed, B. (2019) An Overview of Bag of Words; Importance, Implementation, Applications, and Challenges. pp. 200-204. CrossRef

Li, S. & Gong, B. (2021) Word embedding and text classification based on deep learning methods. MATEC Web of Conferences PY. 336. CrossRef

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. CrossRef

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. CrossRef

Leung H. & Haykin S. (1991) The complex backpropagation algorithm. IEEE Transactions on Signal Processing, 39(9), pp. 2101-2104. CrossRef

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


  • There are currently no refbacks.