Recommendation system for purchasing goods based on the decision tree algorithm
| dc.citation.epage | 127 | |
| dc.citation.issue | Volume 6, № 2 | |
| dc.citation.journalTitle | Advances in Cyber-Physical Systems | |
| dc.citation.spage | 121 | |
| dc.contributor.affiliation | Lviv Polytechnic National University | |
| dc.contributor.author | Kohut, Yurii | |
| dc.contributor.author | Yurchak, Iryna | |
| dc.date.accessioned | 2022-12-01T09:35:36Z | |
| dc.date.available | 2022-12-01T09:35:36Z | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2022 | |
| dc.description.abstract | Over the past few years, interest in applications related to recommendation systems has increased significantly. Many modern services create recommendation systems that, based on user profile information and his behavior. This services determine which objects or products may be interesting to users. ecommendation systems are a modern tool for understanding customer needs. The main methods of constructing recommendation systems are the content-based filtering method and the collaborative filtering method. This article presents the implementation of these methods based on decision trees. The content-based filtering method is based on the description of the object and the customer's preference profile. An object description is a finite set of its descriptors, such as keywords, binary descriptors, etc., and a preference profile is a weighted vector of object descriptors in which scales reflect the importance of each descriptor to the client and its contribution to the final decision. This model selects items that are similar to the customer's favorite items before. The second model, which implements the method of collaborative filtering, is based on information about the history of behavior of all customers on the resource: data on their purchases, ssessments of product quality, reviews, marked product. The model finds clients that are similar in behavior and the recommendation is based on their assessments of this element. Voting was used to combine the results issued by individual models – the best result is chosen from the results of two models of the ensemble. This approach minimizes the impact of randomness and averages the errors of each model. The aim: The purpose of work is to create real competitive ecommendation system for short period of time and minimum costs. | |
| dc.format.pages | 121-127 | |
| dc.identifier.citation | Kohut Yu. Recommendation system for purchasing goods based on the decision tree algorithm / Yurii Kohut, Iryna Yurchak // Advances in Cyber-Physical Systems. – Lviv : Lviv Politechnic Publishing House, 2021. – Volume 6, № 2. – P. 121–127 . – Bibliography: 12 titles. | |
| dc.identifier.doi | https://doi.org/10.23939/acps2021.02.121 | |
| dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/57242 | |
| dc.language.iso | en | |
| dc.publisher | Lviv Politechnic Publishing House | |
| dc.relation.ispartof | Advances in Cyber-Physical Systems | |
| dc.relation.references | [1] Mayer-Schoenberger W. Big data. A revolution that will change the way we live, work and think / Victor Mayer-Schoenberger, Kenneth Kukier; lane. with English Inna Gaidyuk. – М.: Mann, Ivanov and Ferber, 2014. – p. 240. Available at: https://doi.org/10.1016/j.jvcir.2019.102705. (Accessed: 18 November 2021). [2] A. Pal, P. Parhi and M. Aggarwal. An improved content based collaborative filtering algorithm for movie recommendations. 2017 Tenth International Conference on Contemporary Computing (IC3), 2017, pp. 1–3, doi: 10.1109/IC3.2017.8284357. [3] Ajah, I.A. Nweke, H.F. Big Data and Business Analytics: Trends, Platforms, Success Factors and Applications. Big Data Cogn. Comput. 2019, 3, 32. https://doi.org/10.3390/bdcc3020032. [4] Y. Roh, G. Heo and S. E. Whang. A Survey on Data Collection for Machine Learning: A Big Data – AI Integration Perspective. in IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 4, pp. 1328–1347, 1 April 2021, doi: 10.1109/TKDE.2019.2946162. [5] Lin, J., Zhong, C., Hu, D., Rudin, C. & Seltzer, M.. (2020). Generalized and Scalable Optimal Sparse Decision Trees. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:6150–6160. Available at: https://proceedings.mlr.press/ v119/lin20g.html. (Accessed: 18 November 2021). [6] Avellaneda, F. (2020). Efficient Inference of Optimal Decision Trees. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 3195–3202. https://doi.org/10.1609/ aaai.v34i04.5717. [7] Kingsford, C., Salzberg, S. What are decision trees?. Nat Biotechnol 26, 1011–1013 (2008). https://doi.org/10.1038/ nbt0908-1011. [8] Tanha, J., van Someren, M. & Afsarmanesh, H. Semi-supervised self-training for decision tree classifiers. Int. J. Mach. Learn. & Cyber. 8, 355–370 (2017). https://doi.org/10.1007/s13042-015- 0328-7. [9] Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification And Regression Trees (1st ed.). Routledge. https://doi.org/10.1201/9781315139470. [10] R. Rivera-Lopez and J. Canul-Reich. Construction of Near Optimal Axis-Parallel Decision Trees Using a Differential Evolution-Based Approach. in IEEE Access, vol. 6, pp. 5548– 5563, 2018, doi: 10.1109/ACCESS.2017.2788700. [11] Oksana Svystun, Iryna Yurchak. Recommendation Dialog System for Selecting the Computer Hardware Configuration. Advances in Cyber-Physical Systems, Volume 6, Number 1, 2021, pp. 70–76. ISSN: 2524-0382 (print), 2707-0069 (online) DOI: https://doi.org/10.23939/acps2021.01.070. [12] M.Mohri, A.Rostamizadeh, A.Talwalkar. Foundations of Machine Learning, second edition. MIT Press, Second Edition, 2018. Available at: https://doi.org/10.1016/j.jvcir.2019.102705. (Accessed: 18 November 2021). | |
| dc.subject | Recommendation system, Decisions tree, Quick decision making, Big Data, Analysis of Big Data | |
| dc.title | Recommendation system for purchasing goods based on the decision tree algorithm | |
| dc.type | Article |
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