计算机科学
循环神经网络
相似性(几何)
规范化(社会学)
文字2vec
人工智能
人工神经网络
嵌入
数据挖掘
机器学习
图像(数学)
社会学
人类学
作者
Mu-Fan Wang,Yi-Shu Lu,Jiun‐Long Huang
标识
DOI:10.1109/bigcomp.2019.8679431
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI