A route recommendation method considering individual user’s preferences by monte-carlo tree search and its evaluations

As smartphones and tablets are widely spread and used, route recommendation and guidance services have become commonplace. Conventional services in route recommendation and guidance try to give best routes in terms of route length, time required, and train/bus fares, whereas even different users are given the same route when inputting the same parameters. However, each user has various preferences from the aspect of safety and comfort. It is strongly desirable to reflect the user’s preferences in route recommendation and recommend the most preferable route to every user. Since user’s preferences are extremely vague and complicated, how to evaluate them in route recommendation is one of the key problems there. In this paper, we propose a route recommendation method, called P-UCT method, considering individual user’s preferences utilizing Monte-Carlo tree search. In the proposed method, we firstly ex-tract route features based on the route recommendation history of every user and construct a route evaluator based on Support Vector Machine (SVM). After that, the method generates a random route from a start point to an end point by Monte-Carlo tree search. The route evaluator determines how well every generated route matches the user’s preferences. By repeating the evaluation, the method obtains the route, which must be closest to the user’s preferences. Experimental results demonstrate that the proposed method outperforms the existing method from the viewpoint of the average evaluation scores. They also demonstrate that the proposed method provides the recommended route reflecting the user’s individual preferences even if it learns the recommended route history of areas in different situations.