An attentional recurrent neural network for personalized next location recommendation

Qing Guo, Zhu Sun, Jie Zhang, Yin Leng Theng

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

Most existing studies on next location recommendation propose to model the sequential regularity of check-in sequences, but suffer from the severe data sparsity issue where most locations have fewer than five following locations. To this end, we propose an Attentional Recurrent Neural Network (ARNN) to jointly model both the sequential regularity and transition regularities of similar locations (neighbors). In particular, we first design a meta-path based random walk over a novel knowledge graph to discover location neighbors based on heterogeneous factors. A recurrent neural network is then adopted to model the sequential regularity by capturing various contexts that govern user mobility. Meanwhile, the transition regularities of the discovered neighbors are integrated via the attention mechanism, which seamlessly cooperates with the sequential regularity as a unified recurrent framework. Experimental results on multiple real-world datasets demonstrate that ARNN outperforms state-of-the-art methods.

Original languageEnglish
Title of host publicationThe 34th AAAI Conference on Artificial Intelligence (AAAI)
Place of PublicationPalo Alto, CA
PublisherAssociation for the Advancement of Artificial Intelligence
Pages83-90
Number of pages8
ISBN (Print)9781577358350
DOIs
Publication statusPublished - 2020
Externally publishedYes
EventAAAI Conference on Artificial Intelligence (34th : 2020) - New York, United States
Duration: 7 Feb 202012 Feb 2020

Conference

ConferenceAAAI Conference on Artificial Intelligence (34th : 2020)
Abbreviated titleAAAI 20
CountryUnited States
CityNew York
Period7/02/2012/02/20

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  • Cite this

    Guo, Q., Sun, Z., Zhang, J., & Theng, Y. L. (2020). An attentional recurrent neural network for personalized next location recommendation. In The 34th AAAI Conference on Artificial Intelligence (AAAI) (pp. 83-90). Palo Alto, CA: Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v34i01.5337