MPTR: A maximal-marginal-relevance-based personalized trip recommendation method

Wenjing Luan, Guanjun Liu, Changjun Jiang, Mengchu Zhou

Research output: Contribution to journalArticlepeer-review

37 Scopus citations


Personalized trip recommendation has drawn much attention recently with the development of location-based services. How to utilize the data in the location-based social network to recommend a single Point of Interest (POI) or a sequence of POIs for users is an important question to answer. Recommending the latter is called trip recommendation that is a challenging study because of the diversity of trips and complexity of involved computation. This work proposes a maximal-marginal-relevance-based personalized trip recommendation method that considers both relevance and diversity of trips in trip planning. An ant-colony-optimization-based trip planning algorithm is developed to efficiently plan a trip. Finally, case studies and experiments illustrate the effectiveness of our method.

Original languageEnglish (US)
Article number8306447
Pages (from-to)3461-3474
Number of pages14
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number11
StatePublished - Nov 2018

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


  • Ant colony optimization
  • Location-based social networks
  • Personalized trip recommendation


Dive into the research topics of 'MPTR: A maximal-marginal-relevance-based personalized trip recommendation method'. Together they form a unique fingerprint.

Cite this