Mining GPS data for trajectory recommendation

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

Research output: Contribution to journalConference article

18 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)50-61
Number of pages12
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8444 LNAI
Issue numberPART 2
DOIs
StatePublished - Jan 1 2014
Event18th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2014 - Tainan, Taiwan, Province of China
Duration: May 13 2014May 16 2014

Fingerprint

Global positioning system
Mining
Recommendations
Trajectories
Trajectory
Trace
Internet
Generative Models
User Profile
User Preferences
Sensors
Experiments
Sensor
Evaluate

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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title = "Mining GPS data for trajectory recommendation",
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.",
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Mining GPS data for trajectory recommendation. / Yin, Peifeng; Ye, Mao; Lee, Wang Chien; Li, Zhenhui.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 8444 LNAI, No. PART 2, 01.01.2014, p. 50-61.

Research output: Contribution to journalConference article

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