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

研究成果: Conference contribution同行評審

18 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781538677896
DOIs
出版狀態Published - 1 4月 2019
事件2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Kyoto, Japan
持續時間: 27 2月 20192 3月 2019

出版系列

名字2019 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
國家/地區Japan
城市Kyoto
期間27/02/192/03/19

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