Content-aware hierarchical point-of-interest embedding model for successive POI recommendation

Buru Chang, Yonggyu Park, Donghyeon Park, Seongsoon Kim, Jaewoo Kang

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

38 Citations (Scopus)

Abstract

Recommending a point-of-interest (POI) a user will visit next based on temporal and spatial context information is an important task in mobile-based applications. Recently, several POI recommendation models based on conventional sequential-data modeling approaches have been proposed. However, such models focus on only a user's check-in sequence information and the physical distance between POIs. Furthermore, they do not utilize the characteristics of POIs or the relationships between POIs. To address this problem, we propose CAPE, the first content-aware POI embedding model which utilizes text content that provides information about the characteristics of a POI. CAPE consists of a check-in context layer and a text content layer. The check-in context layer captures the geographical influence of POIs from the check-in sequence of a user, while the text content layer captures the characteristics of POIs from the text content. To validate the efficacy of CAPE, we constructed a large-scale POI dataset. In the experimental evaluation, we show that the performance of the existing POI recommendation models can be significantly improved by simply applying CAPE to the models.

Original languageEnglish
Title of host publicationProceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018
EditorsJerome Lang
PublisherInternational Joint Conferences on Artificial Intelligence
Pages3301-3307
Number of pages7
ISBN (Electronic)9780999241127
DOIs
Publication statusPublished - 2018
Event27th International Joint Conference on Artificial Intelligence, IJCAI 2018 - Stockholm, Sweden
Duration: 2018 Jul 132018 Jul 19

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume2018-July
ISSN (Print)1045-0823

Other

Other27th International Joint Conference on Artificial Intelligence, IJCAI 2018
CountrySweden
CityStockholm
Period18/7/1318/7/19

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Chang, B., Park, Y., Park, D., Kim, S., & Kang, J. (2018). Content-aware hierarchical point-of-interest embedding model for successive POI recommendation. In J. Lang (Ed.), Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018 (pp. 3301-3307). (IJCAI International Joint Conference on Artificial Intelligence; Vol. 2018-July). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2018/458