Improvements on Probability Model for Sightseeing Route Recommendation Method Employing Generalized Formulation in terms of Edges This paper proposes an improved formulation of probability model for sightseeing route recommendation method based on a generalized formulation in terms of edges. In sightseeing route recommendation, at least, methods has to predict spots to be visited by a user and a route to travel them. Recommendation process can be modeled as a optimization problem, such as Traveling Salesman Problem or Orienteering Problem. Simulated annealing (SA) is one of the metaheuristics to solve optimization problem, in which problems can be modeled as sampling from a probability model. A method using SA on Edge vectorbased formulation (EV) has been proposed, which has introduced a concept of a real and virtual edge to deal with a noncomplete graph like a real world road map. Using this formulation, EV has generalized both spots and roads passed by a user to edges, then applied SA to its formulated problem. The shortage of EV is that this model deals with the real and virtual edges asymmetrically, which causes decreasing of performance. This paper proposes an improved formulation of the objective function of EV. Experimental result shows that asymmetry treatment actually decreases performance, and proposed formulation gives grater performance than that of original formulation.

A Route Recommendation Method Based on Personal Preferences by MonteCarlo Tree Search In this paper, we propose a route recommendation method, called PUCT method, considering individual userxe2x80x99s preferences utilizing MonteCarlo tree search. In the proposed method, we firstly extract 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 MonteCarlo tree search. The route evaluator determines how well every generated route matches the userxe2x80x99s preferences. By repeating the evaluation, the method obtains the route, which must be closest to the userxe2x80x99s preferences. Experimental results demonstrate that the proposed method outperforms the existing method from the viewpoint of the average evaluation scores.