An interactive multi-task learning framework for next POI recommendation with uncertain check-ins

Lu Zhang, Zhu Sun*, Jie Zhang, Yu Lei, Chen Li, Ziqing Wu, Horst Kloeden, Felix Klanner

*Corresponding author for this work

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

Abstract

Studies on next point-of-interest (POI) recommendation mainly seek to learn users' transition patterns with certain historical check-ins. However, in reality, users' movements are typically uncertain (i.e., fuzzy and incomplete) where most existing methods suffer from the transition pattern vanishing issue. To ease this issue, we propose a novel interactive multi-task learning (iMTL) framework to better exploit the interplay between activity and location preference. Specifically, iMTL introduces: (1) temporal-aware activity encoder equipped with fuzzy characterization over uncertain check-ins to unveil the latent activity transition patterns; (2) spatial-aware location preference encoder to capture the latent location transition patterns; and (3) task-specific decoder to make use of the learned latent transition patterns and enhance both activity and location prediction tasks in an interactive manner. Extensive experiments on three real-world datasets show the superiority of iMTL.
Original languageEnglish
Title of host publicationProceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
EditorsChristian Bessiere
Place of PublicationSan Francisco, CA
PublisherInternational Joint Conferences on Artificial Intelligence
Pages3551-3557
Number of pages7
ISBN (Electronic)9780999241165
DOIs
Publication statusPublished - 2020
Event29th International Joint Conference on Artificial Intelligence, IJCAI 2020 - Yokohama, Japan
Duration: 11 Jul 202017 Jul 2020

Conference

Conference29th International Joint Conference on Artificial Intelligence, IJCAI 2020
Abbreviated titleIJCAI
CountryJapan
CityYokohama
Period11/07/2017/07/20

Keywords

  • Recommender Systems
  • Personalization and User Modeling

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  • Cite this

    Zhang, L., Sun, Z., Zhang, J., Lei, Y., Li, C., Wu, Z., ... Klanner, F. (2020). An interactive multi-task learning framework for next POI recommendation with uncertain check-ins. In C. Bessiere (Ed.), Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (pp. 3551-3557). San Francisco, CA: International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2020/491