Multi-Objective Optimal Travel Route Recommendation for Tourists by Improved Ant Colony Optimization Algorithm

Abstract

With the swift development of tourism all around the world, it has become vital to improve the recommendation of useful travel information to tourists to assure their convenience and satisfaction. In this paper, we propose a novel multi-objective optimal travel route recommendation framework, which collects tourists’ travel trajectories from their mobile phone signaling data. Then, the proposed framework preprocesses the mobile signaling data to transform raw trajectories into tourists’ travel sequences. Subsequently, the framework finds the popular attractions and frequent travel routes from the travel pattern sequences by using a frequent pattern mining method. Finally, an improved ant colony optimization (ACO) algorithm with a novel extensible heuristic factor approach is adopted to search the multi-objective optimal travel routes according to the popularity of attractions and travel time of tourists. The experimental results indicate that the proposed framework is efficient in recommending multi-objective optimal travel routes considering tourists’ travel time and attractions’ popularity while ensuring that the recommended travel route is suitable.

An RFID-Enabled IoT-Based Smart Tourist Route Recommendation Algorithm

“…The provision of a personalized online travel recommendation platform is popular among users and meets the specific needs of each user is therefore required [11][12][13][14]. This is provided by the travel service providers to recommend a travel path from a given entrance to an exit in a scenic spot that contains as many POIs as possible [15,16].…”