Enriching Experiences Through Shared Moments: Travel Recommendation for Heterogeneous Users Using Ising Machines

The heterogeneous users travel planning problem (HUTPP) is an optimization problem and it seeks to identify the routes maximizing shared experiences while managing the conflicts over points of interest (POIs) between different users. Conventional methods and solvers using von Neumann computers offer good approximate solutions to optimization problems, but the solution quality may not be guaranteed with limited time costs. On the other hand, Ising machines or quantum annealing machines are non-von Neumann computers that are effective in solving complicated optimization problems. In this paper, we propose an Ising-based method to effectively solve the HUTPP and evaluate the proposed method on a real-world area, Sapporo, Japan. According to the comparison with a conventional method and a conventional solver, the empirical results suggest that the proposed methods outperform the conventional method and solver regarding solution quality, especially with a > 14 % improvement in POI sharing degree.