Open Access

ARTICLE

Research on Tourist Routes Recommendation Based on the User Preference Drifting Over Time

by Chunjing Xiao, Yongwei Qiao, Kewen Xia, Yuxiang Zhang

1 School of Electronics & Information Engineering, Hebei University of Technology, School of Computer Science & Technology, Civil Aviation University of China, Tianjin 300401, China. E-mail: kwxia@hebut.edu.cn
2 Engineering & Technical Training Center, Civil Aviation University of China, Tianjin 300300, China
3 School of Computer Science & Technology, Civil Aviation University of China, Tianjin 300401, China

* Corresponding Author: E-mail:

Computer Systems Science and Engineering 2018, 33(2), 95-103. https://doi.org/10.32604/csse.2018.33.095

Abstract

Tourist routes recommendation is a way to improve the tourist experience and the efficiency of tourism companies. Session-based methods divide all users’ interaction histories into the same number sessions with fixed time window and treat the user preference as time sequences. There have few or even no interaction in some sessions for some users because of the high sparsity and temporal characteristics of tourist data. That lead to many session-based methods can not be applied to routes recommendation due to aggravate the sparsity. In order to better adapt and apply the characteristics of tourism data and alleviate the sparsity, a tourist routes recommendation method based on the user preference drifting over time is proposed. Firstly, the sparsity, temporal context, tourist age and price characteristics of tourism data are analyzed on a real tourism data. Secondly, based on the results of analysis, tourist interaction history is dynamic divided into different number of sessions and the tourist’s evolving profile is then constructed by mining his probabilistic topic distribution in each session using Latent Dirichlet Allocation (LDA) and the time penalty weights. Then, the tourist feature vector based on the tourist age, the price and season of his tourism is modeled and a set of nearest neighbors and the candidate routes is selected base on it. Finally, the routes are recommended according to the similarities of probabilistic topic distributions between the active tourist and routes. Experimental results show that the proposed method can not only effectively adapt to the characteristics of tourism data, but also improve the effect of recommendation.

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Citations