SPENT: A Successive POI Recommendation Method Using Similarity-Based POI Embedding and Recurrent Neural Network with Temporal Influence

Mu Fan Wang, Yi Shu Lu, Jiun-Long Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In recent years, successive Point-of-Interest (POI) recommendation has attracted more and more attention and many methods have been proposed to address the problem of successive POI recommendation. In this paper, we propose the SPENT method which uses similarity tree to organize all POIs and applies Word2Vec to perform POI embedding. Then, SPENT uses a recurrent neural network (RNN) to model users' successive transition behavior. We also propose to insert a bath normalization layer in front of the LSTM and a temporal distance gate in the back of the LSTM to improve the performance of SPENT. To compare the performance of SPENT and other prior successive POI recommendation methods, several experiments are conducted on two real datasets, Gowalla and Foursquare. Experimental results show that SPENT outperforms the other prior methods in terms of precision and recall.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538677896
DOIs
StatePublished - 1 Apr 2019
Event2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Kyoto, Japan
Duration: 27 Feb 20192 Mar 2019

Publication series

Name2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings

Conference

Conference2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019
CountryJapan
CityKyoto
Period27/02/192/03/19

Keywords

  • Successive POI recommendation
  • embedding
  • recommendation
  • recurrent neural network

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    Wang, M. F., Lu, Y. S., & Huang, J-L. (2019). SPENT: A Successive POI Recommendation Method Using Similarity-Based POI Embedding and Recurrent Neural Network with Temporal Influence. In 2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings [8679431] (2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIGCOMP.2019.8679431