Social itinerary recommendation from user-generated digital trails

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Planning travel to unfamiliar regions is a difficult task for novice travelers. The burden can be eased if the resident of the area offers to help. In this paper, we propose a social itinerary recommendation by learning from multiple user-generated digital trails, such as GPS trajectories of residents and travel experts. In order to recommend satisfying itinerary to users, we present an itinerary model in terms of attributes extracted from user-generated GPS trajectories. On top of this itinerary model, we present a social itinerary recommendation framework to find and rank itinerary candidates. We evaluated the efficiency of our recommendation method against baseline algorithms with a large set of user-generated GPS trajectories collected from Beijing, China. First, systematically generated user queries are used to compare the recommendation performance in the algorithmic level. Second, a user study involving current residents of Beijing is conducted to compare user perception and satisfaction on the recommended itinerary. Third, we compare mobile-only approach with Mobile+Cloud architecture for practical mobile recommender deployment. Lastly, we discuss personalization and adaptation factors in social itinerary recommendation throughout the paper.
Publisher
SPRINGER LONDON LTD
Issue Date
2012-06
Language
English
Article Type
Article
Keywords

SYSTEM; CITY

Citation

PERSONAL AND UBIQUITOUS COMPUTING, v.16, no.5, pp.469 - 484

ISSN
1617-4909
DOI
10.1007/s00779-011-0419-8
URI
http://hdl.handle.net/10203/103365
Appears in Collection
GCT-Journal Papers(저널논문)
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