Recommender systems are becoming an intrinsic part of our lives. Currently, more and more people are usingrecommender systems to receive product or service recommendations. This became possible with the increasing powerof mobile devices, the widespread use of the Internet and the accumulation of data about user activity. The selection of asuitable machine learning algorithm for a recommender system is a difficult task due to a large number of algorithmsdescribed in the literature. This task is even more complicated for specific systems, such as a recommender system fortravel by public transport due to the small number of studies in this area. The objective of this paper is to evaluatemachine 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 theestimation algorithm proposed by Yu.I. Zhuravlev. In addition to accuracy, machine learning algorithms have been ratedfor performance. We also presented a possible visualization option on the map of user-preferred stops. The experimentswere conducted on real data from the mobile application “Pribyvalka-63”. The mobile application is a part of thetosamara.ru service, currently used to inform Samara residents about the public transport movement.
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