Travel Route Recommendation by Considering User Transition Patterns

Authors

  • Junjie Sun Department of Social Informatics Graduate School of Informatics Kyoto University
  • Chenyi Zhuang National Institute of Advanced Industrial Science and Technology
  • Qiang Ma Department of Social Informatics Graduate School of Informatics Kyoto University

Keywords:

travel route recommendation, LBSN, sightseeing, matrix factorization

Abstract

Travel route recommendation services that recommend a sequence of points-of-interest (POIs) for tourists are very useful in location-based social networks (LBSNs). Currently, most of the work that addresses this task are focusing on personalization and POI features, which estimate user-location relations while rarely considering transitions, i.e., the relationships between locations. To this end, we propose a latent factorization model that learns transition patterns with enhanced spatial-temporal features between locations. Furthermore, we recommend travel routes by combining knowledge on locations and transitions. Experimental results with public datasets reveal that our approaches improve upon the performance of conventional methods.

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Published

2019-01-30

How to Cite

Sun, J., Zhuang, C. and Ma, Q. (2019) “Travel Route Recommendation by Considering User Transition Patterns”, e-Review of Tourism Research, 16(2/3). Available at: https://ertr-ojs-tamu.tdl.org/ertr/article/view/320 (Accessed: 18 April 2024).

Issue

Vol. 16 No. 2/3 (2019)

Section

Articles

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