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Development and research of algorithms for determining user preferred public transport stops in a geographic information system based on machine learning methods
A.A. Borodinov 1
1 Samara National Research University, 443086, Samara, Russia, Moskovskoye Shosse 34
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Full text of article: Russian language.
The paper considers a problem of determining the user preferred stops in a public transport recommender system. The effectiveness of using various machine learning methods to solve this problem in a system of personalized recommendations is compared, including a support vector method, a decision tree, a random forest, AdaBoost, a k-nearest neighbors algorithm, and a multi-layer perceptron. The described traditional methods of machine learning are also compared with the method proposed herein and based on an estimate calculation algorithm. The efficiency and the effectiveness of the proposed method are confirmed in the work.
recommender system, machine learning, user preferences.
Borodinov AA. Development and research of algorithms for determining user preferred public transport stops in a geographic information system based on machine learning methods. Computer Optics 2020; 44(4): 646-652. DOI: 10.18287/2412-6179-CO-713.
The work was funded by the Ministry of Science and Higher Education of the Russian Federation (unique project identifier RFMEFI57518X0177).
- Campigotto P, Rudloff C, Leodolter M, Bauer D. Personalized and situation-aware multimodal route recommendations: The FAVOUR algorithm. IEEE Trans Intell Transp Syst 2017; 18: 92-102. DOI: 10.1109/TITS.2016.2565643.
- Agafonov AA, Myasnikov VV. Numerical route reservation method in the geoinformatic task of autonomous vehicle routing. Computer Optics 2018; 42(5): 912-920. DOI: 10.18287/2412-6179-2018-42-5-912-920.
- Agafonov AA, Yumaganov AS, Myasnikov VV. Big data analysis in a geoinformatic problem of short-term traffic flow forecasting based on a K nearest neighbors method. Computer Optics 2018; 42(6): 1101-1111. DOI: 10.18287/2412-6179-2018-42-6-1101-1111.
- Portugal I, Alencar P, Cowan D. The use of machine learning algorithms in recommender systems: A systematic review. Expert Syst Appl 2018; 97: 205-227. DOI: 10.1016/j.eswa.2017.12.020.
- Li X, Wang Z, Wang L, Hu R, Zhu Q. A multi-dimensional context-aware recommendation approach based on improved random forest algorithm. IEEE Access 2018; 6: 45071-45085. DOI: 10.1109/ACCESS.2018.2865436.
- Bogaert M, Lootens J, Van den Poel D, Ballings M. Evaluating multi-label classifiers and recommender systems in the financial service sector. Eur J Oper Res 2019; 279(2): 620-634. DOI: 10.1016/j.ejor.2019.05.037.
- Kim H, Yang G, Jung H, Lee SH, Ahn JJ. An intelligent product recommendation model to reflect the recent purchasing patterns of customers. Mob Netw Appl 2019; 24(1): 163-170. DOI: 10.1007/s11036-017-0986-7.
- Wang X, Wen J, Luo F, Zhou W, Ren H. Personalized recommendation system based on support vector machine and particle swarm optimization. In Book: Zhang S, Wirsing M, Zhang Z, eds. KSEM 2015: Knowledge science, engineering and management. Heidelberg, New York: Springer; 2015: 489-495. DOI: 10.1007/978-3-319-25159-2_44.
- Jiamthapthaksin R, Aung TH. User preferences profiling based on user behaviors on Facebook page categories, International Conference on Knowledge and Smart Technology: Crunching Information of Everything 2017: 248-253. DOI: 10.1109/KST.2017.7886077.
- Marović M, Mihoković M, Mikša M, Pribil S, Tus A. Automatic movie ratings prediction using machine learning. 34th International Convention on Information and Communication Technology, Electronics and Microelectronics 2011: 1640-1645.
- Ivan I, Horák J, Zajíčková L, Burian J, Fojtík D. Factors influencing walking distance to the preferred public transport stop in selected urban centres of Czechia. GeoScape 2019; 13(1): 16-30. DOI: 10.2478/geosc-2019-0002.
- Borodinov AA, Myasnikov VV. Analysis of the preferences of public transport passengers in the task of building a personalized recommender system. CEUR Workshop Proc 2019; 2391: 198-205. DOI: 10.18287/1613-0073-2019-2391-198-205
- Zhuravlev YuI, Gourevich IB. Pattern recognition and image recognition [In Russian]. In Book: Zhuravlev YuI, ed. Recognition, classification, prediction. Mathematical methods and their application. Issue 2. Moscow: "Nauka" Publisher; 1989: 5-72.
- Supervised learning – scikit-learn 0.22.2 documentation. Source: <https://scikit-learn.org/stable/supervised_learning.html>.
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