User Transition Pattern Analysis for Travel Route Recommendation

Junjie SUN  Chenyi ZHUANG  Qiang MA  

IEICE TRANSACTIONS on Information and Systems   Vol.E102-D   No.12   pp.2472-2484
Publication Date: 2019/12/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2019EDP7096
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
travel route recommendation,  sightseeing,  location-based social network,  matrix factorization,  

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A travel route recommendation service that recommends a sequence of points of interest for tourists traveling in an unfamiliar city is a very useful tool in the field of location-based social networks. Although there are many web services and mobile applications that can help tourists to plan their trips by providing information about sightseeing attractions, travel route recommendation services are still not widely applied. One reason could be that most of the previous studies that addressed this task were based on the orienteering problem model, which mainly focuses on the estimation of a user-location relation (for example, a user preference). This assumes that a user receives a reward by visiting a point of interest and the travel route is recommended by maximizing the total rewards from visiting those locations. However, a location-location relation, which we introduce as a transition pattern in this paper, implies useful information such as visiting order and can help to improve the quality of travel route recommendations. To this end, we propose a travel route recommendation method by combining location and transition knowledge, which assigns rewards for both locations and transitions.