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

Document Type

Article

Publication Date

11-1-2018

Abstract

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.

Identifier

85042882957 (Scopus)

Publication Title

IEEE Transactions on Intelligent Transportation Systems

External Full Text Location

https://doi.org/10.1109/TITS.2017.2781138

ISSN

15249050

First Page

3461

Last Page

3474

Issue

11

Volume

19

Grant

16511100900

Fund Ref

National Natural Science Foundation of China

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