NEXT: a neural network framework for next POI recommendation

doi:10.1007/s11704-018-8011-2

Front. Comput. Sci.

2020, Vol. 14

Issue (2) : 314-333 https://doi.org/10.1007/s11704-018-8011-2

RESEARCH ARTICLE

NEXT: a neural network framework for next POI recommendation

Zhiqian ZHANG¹, Chenliang LI¹(

), Zhiyong WU², Aixin SUN³, Dengpan YE¹, Xiangyang LUO⁴

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

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Abstract

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 incorporatemeta-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.

Keywords POI neural networks POI recommendation

Corresponding Author(s): Chenliang LI

Just Accepted Date: 30 November 2018 Online First Date: 17 September 2019 Issue Date: 16 October 2019

Cite this article:

Zhiqian ZHANG,Chenliang LI,Zhiyong WU, et al. NEXT: a neural network framework for next POI recommendation[J]. Front. Comput. Sci., 2020, 14(2): 314-333.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-018-8011-2
https://academic.hep.com.cn/fcs/EN/Y2020/V14/I2/314

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