NEXT:a neural network framework for next POI recommendation

查看全文 作　　者：Zhiqian [1]ZHANG;Chenliang [1]LI;Zhiyong [2]WU;Aixin [3]SUN;Dengpan [1]YE;Xiangyang [4]LUO

机构地区：[1]Key Laboratory of Aerospace Information Security and Trusted Computing,Ministry of Education,School of Cyber Science and Engineering,Wuhan University,Wuhan 430072,China;[2]Department of Computer Science,The University of Hong Kong,Pokfulam Road,Hong Kong 999077,China;[3]School of Computer Science and Engineering,Nanyang Technological University,Singapore 639798,Singapore;[4]State Key Lab of Mathematical Engineering and Advanced Computing,Zhengzhou 450001,China

出　　处：《Frontiers of Computer Science》索引2020年第14卷第2期,共20页

基　　金：the National Natural Science Foundation of China(Grant Nos.61872278,61502344,1636219,U1636101);Natural Science Foundation of Hubei Province(2017CFB502);Academic Team Building Plan for Young Scholars from Wuhan University(Whu2016012);Singapore Ministry of Education Academic Research Fund Tier 2(MOE2014-T2-2-066)。 摘　　要：The task of next POI recommendations has been studied extensively in recent years.However,developing a unified recommendation framework to incorporate multiple factors associated with both POIs and users remains challenging,because of the heterogeneity nature of these information.Further,effective mechanisms to smoothly handle cold-start cases are also a difficult topic.Inspired by the recent success of neural networks in many areas,in this paper,we propose a simple yet effective neural network framework,named NEXT,for next POI recommendations.NEXT is a unified framework to learn the hidden intent regarding user's next move,by incorporating different factors in a unified manner.Specifically,in NEXT,we incorporate meta-data information,e.g.,user friendship and textual descriptions of POIs,and two kinds of temporal contexts(i.e.,time interval and visit time).To leverage sequential relations and geographical influence,we propose to adopt DeepWalk,a network representation learning technique,to encode such knowledge.We evaluate the effectiveness of NEXT against other state-of-the-art alternatives and neural networks based solutions.Experimental results on three publicly available datasets demonstrate that NEXT significantly outperforms baselines in real-time next POI recommendations.Further experiments show inherent ability of NEXT in handling cold-start. 关键词：POI NEURAL networks POI RECOMMENDATION

相关文献

参考文献（50）
引证文献（10）
耦合文献（0）

参考文献(50)

引证文献(10)

互联网出版许可证新出网证(渝)字10号全国400电话 - 免长途话费

NEXT:a neural network framework for next POI recommendation

参考文献(50)

引证文献(10)

用户提示