期刊
PERVASIVE AND MOBILE COMPUTING
卷 18, 期 -, 页码 4-17出版社
ELSEVIER
DOI: 10.1016/j.pmcj.2014.08.002
关键词
Landmark personalization; Landmark clustering; Adaptive recommendations; Social media
资金
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [2013058053]
When travelers plan trips, landmark recommendation systems that consider the trip properties will conveniently aid travelers in determining the locations they will visit. Because interesting locations may vary based on the traveler and the situation, it is important to personalize the landmark recommendations by considering the traveler and the trip. In this paper, we propose an approach that adaptively recommends clusters of landmarks using geo-tagged social media. We first examine the impact of a trip's spatial and temporal properties on the distribution of popular places through large-scale data analyses. In our approach, we compute the significance of landmarks for travelers based on their trip's spatial and temporal properties. Next, we generate clusters of landmark recommendations, which have similar themes or are contiguous, using travel trajectory histories. Landmark recommendation performances based on our approach are evaluated against several baseline approaches. Our approach results in increased accuracy and satisfaction compared with the baseline approaches. Through a user study, we also verify that our approach is applicable to lesser-known places and reflects local events as well as seasonal changes. (C) 2014 Elsevier B.V. All rights reserved.
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