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31 January 2020 Evaluating classifiers to determine user-preferred stops in a personalized recommender system
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Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 114330N (2020) https://doi.org/10.1117/12.2556536
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands
Abstract
Recommender systems are becoming an intrinsic part of our lives. Currently, more and more people are using recommender systems to receive product or service recommendations. This became possible with the increasing power of mobile devices, the widespread use of the Internet and the accumulation of data about user activity. The selection of a suitable machine learning algorithm for a recommender system is a difficult task due to a large number of algorithms described in the literature. This task is even more complicated for specific systems, such as a recommender system for travel by public transport due to the small number of studies in this area. The objective of this paper is to evaluate machine learning algorithms to determine user-preferred stops of public transport in a personalized recommender system. In this paper, we examine some of the most well-known approaches such as support vector machine, the decision tree, random forest, adaboost, k-nearest neighbors algorithm, multi-layer perceptron classifier and approach based on the estimation algorithm proposed by Yu.I. Zhuravlev. In addition to accuracy, machine learning algorithms have been rated for performance. We also presented a possible visualization option on the map of user-preferred stops. The experiments were conducted on real data from the mobile application “Pribyvalka-63”. The mobile application is a part of the tosamara.ru service, currently used to inform Samara residents about the public transport movement.
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A. A. Borodinov and V. V. Myasnikov "Evaluating classifiers to determine user-preferred stops in a personalized recommender system", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114330N (31 January 2020); https://doi.org/10.1117/12.2556536
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