Mining GPS Data for Trajectory Recommendation View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2014

AUTHORS

Peifeng Yin , Mao Ye , Wang-Chien Lee , Zhenhui Li

ABSTRACT

The wide use of GPS sensors in smart phones encourages people to record their personal trajectories and share them with others in the Internet. A recommendation service is needed to help people process the large quantity of trajectories and select potentially interesting ones. The GPS trace data is a new format of information and few works focus on building user preference profiles on it. In this work we proposed a trajectory recommendation framework and developed three recommendation methods, namely, Activity-Based Recommendation (ABR), GPS-Based Recommendation (GBR) and Hybrid Recommendation. The ABR recommends trajectories purely relying on activity tags. For GBR, we proposed a generative model to construct user profiles based on GPS traces. The Hybrid recommendation combines the ABR and GBR. We finally conducted extensive experiments to evaluate these proposed solutions and it turned out the hybrid solution displays the best performance. More... »

PAGES

50-61

Book

TITLE

Advances in Knowledge Discovery and Data Mining

ISBN

978-3-319-06604-2
978-3-319-06605-9

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-06605-9_5

DOI

http://dx.doi.org/10.1007/978-3-319-06605-9_5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1021197511


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