標題: SPENT: A Successive POI Recommendation Method Using Similarity-based POI Embedding and Recurrent Neural Network with Temporal Influence
作者: Wang, Mu-Fan
Lu, Yi-Shu
Huang, Jiun-Long
資訊工程學系
Department of Computer Science
關鍵字: Successive POI recommendation;recurrent neural network;embedding;recommendation
公開日期: 1-一月-2019
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.
URI: http://hdl.handle.net/11536/152465
ISBN: 978-1-5386-7789-6
ISSN: 2375-933X
期刊: 2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (BIGCOMP)
起始頁: 131
結束頁: 138
顯示於類別:會議論文