Adaptive landmark recommendations for travel planning: Personalizing and clustering landmarks using geo-tagged social media |
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| Affiliation: | 1. Defense Agency for Technology and Quality, Republic of Korea;2. Gwangju Institute of Science and Technology, 261 Cheomdangwagi-ro, Buk-gu, Gwangju 500-712, Republic of Korea;1. Department of Physics, Wilson College, Chowpatty, Mumbai 400 007, India;2. Department of Physics and National Centre for Nanosciences and Nanotechnology, University of Mumbai, Santacruz (E), Mumbai 400 098, India;3. Department of Chemistry, University of Mumbai, Santacruz (E), Mumbai 400 098, India;1. Software Engineering Institute, East China Normal University, Shanghai 200062, China;2. Department of Computer Science, University of Texas at Dallas, Richardson, TX 75080, USA;3. Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai 200062, China;1. Graz University of Technology, Austria;2. Microsoft Research Redmond, United States;1. Department of Computer Engineering, Faculty of Engineering, Chiang Mai University, Thailand;2. Sirindhorn International Institute of Technology (SIIT), Thammasat University, Thailand;3. Department of Civil Engineering, School of Engineering, University of Tokyo, Japan;4. School of Tourism Development, Maejo University, Thailand |
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| Abstract: | 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. |
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| Keywords: | Landmark personalization Landmark clustering Adaptive recommendations Social media |
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