Hybrid Tourism Recommendation System: A Multi-Objective Perspective
Shenqing Wang, Ruifen Cao, Ye Tian, Chun-Hou Zheng · 2022 IEEE Congress on Evolutionary Computation (CEC) · 2022
A smart recommendation method can greatly improve tourists' travel experience, and it is an important task for tourism recommendation systems to intelligently suggest scenic spots for tourists according to their historical visit records. Currently, the collaborative filtering and deep neural network-based methods occupy the mainstream of tourism recommendation systems. Although each type of recommendation methods is superior over the others in terms of different aspects, the performance of a single recommendation method is limited. In order to inherit the advantages of different types of recommendation methods, this work suggests a hybrid method for assembling multiple methods for tourism recommendation. Based on the scenic spots obtained by multiple recommendation methods, the proposed hybrid method uses two novel objectives to evaluate each scenic spot, and identifies the best$K$scenic spots via the techniques used in evolutionary multi-objective optimization. In comparison to existing recommendation methods and hybrid methods, the proposed hybrid method exhibits better performance on two public tourism datasets and a new dataset created based on the tourism information of Huangshan City.