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NEXT: a neural network framework for next POI recommendation

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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 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.

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Acknowledgments

This research was supported by 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) and Singapore Ministry of Education Academic Research Fund Tier 2 (MOE2014-T2-2-066). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X GPU used for this research.

Author information

Authors and Affiliations

  1. Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, 430072, China

    Zhiqian Zhang, Chenliang Li & Dengpan Ye

  2. Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong, 999077, China

    Zhiyong Wu

  3. School of Computer Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    Aixin Sun

  4. State Key Lab of Mathematical Engineering and Advanced Computing, Zhengzhou, 450001, China

    Xiangyang Luo

Authors
  1. Zhiqian Zhang
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  2. Chenliang Li
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  3. Zhiyong Wu
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  4. Aixin Sun
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  5. Dengpan Ye
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  6. Xiangyang Luo
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Corresponding author

Correspondence to Chenliang Li.

Additional information

Zhiqian Zhang is currently a Master student at Wuhan University, under the supervision of Dr. Chenliang Li. She received Bachelor degree from Wuhan University, China in 2015. Her research interests include natural language processing, information retrieval, data mining, and social media analysis and mining.

Chenliang Li received PhD from Nanyang Technological University, Singapore in 2013. Currently, he is an associate professor at School of Cyber Science and Engineering, Wuhan University, China. His research interests include information retrieval, text/Web mining, data mining, and natural language processing. His papers appear in SIGIR, CIKM, ACL, AAAI, TOIS, TKDE, and JASIST.

Zhiyong Wu is a currently PhD student at Department of Computer Science, the University of Hong Kong, China. He received Bachelor degree from Wuhan University, China in 2017. His research interests include data mining, natural language processing, and database.

Aixin Sun is an associate professor with School of Computer Engineering, Nanyang Technological University, Singapore. He received PhD from the same school in 2004. His research interests include information retrieval, text mining, social computing, and multimedia. His papers appear in major international conferences like SIGIR, KDD, WSDM, ACM Multimedia, and journals including TOIS, TKDE, and JASIST.

Dengpan Ye is currently a professor in School of Cyber Science and Engineering, Wuhan University, China. He received the BASc in automatic control from SCUT in 1996 and PhD degree at NJUST in 2005 respectively. He worked as a Post-Doctoral Fellow in Information System School of Singapore Management University, Singapore.

His research interests include machine learning and multimedia security. He is the author or co-author of more than 30 refereed journal and conference papers.

Xiangyang Luo is currently a professor at Zhengzhou Science and Technology Institute and the State Key Laboratory of Mathematical Engineering and Advanced Computing, China. His research interests lie in multimedia security and cyberspace surveying and mapping. He is the author or co-author of more than 100 refereed international journal and conference papers. He has obtained the support of the National Natural Science Foundation of China and the National Key R&D Program of China.

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Zhang, Z., Li, C., Wu, Z. et al. NEXT: a neural network framework for next POI recommendation. Front. Comput. Sci. 14, 314–333 (2020). https://doi.org/10.1007/s11704-018-8011-2

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