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- Title
Research on Tourist Routes Recommendation Based on the User Preference Drifting Over Time.
- Authors
Chunjing Xiao; Yongwei Qiao; Kewen Xia; Yuxiang Zhang
- Abstract
Tourist routes recommendation is away 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 route srecommendation 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 actve tourist and routes. Experimental results show that the proposed method can not only effectively adapt tothe characteristics of tourism data, but also improve the effect of recommendation.
- Subjects
TOURISM; RECOMMENDER systems; TOURISTS; CONSUMER preferences; FEATURE extraction
- Publication
Computer Systems Science & Engineering, 2018, Vol 33, Issue 2, p95
- ISSN
0267-6192
- Publication type
Article
- DOI
10.32604/csse.2018.33.095