Content-aware successive point-of-interest recommendation

Buru Chang, Yookyung Koh, Donghyeon Park, Jaewoo Kang

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

Abstract

Successive point-of-interest (POI) recommendation based on user check-in histories plays an important role in mobile-based social media platforms. Although a large amount of check-in data including textual content is generated from such platforms, most successive POI recommendation models do not leverage textual contents that provide useful information for understanding user interests. To address this problem, we propose a new content-aware successive POI recommendation (CAPRE) model in this paper. Based on a multi-head attention mechanism and a character-level convolutional neural network, CAPRE encodes user-generated textual contents into content embedding to capture user interests. Based on long short-term memories (LSTMs), CAPRE capture content-aware user behavior patterns from encoded content embedding. Evaluation results on real-world datasets show that CAPRE achieves state-of-the-art recommendation performance.

Original languageEnglish
Title of host publicationProceedings of the 2020 SIAM International Conference on Data Mining, SDM 2020
EditorsCarlotta Demeniconi, Nitesh Chawla
PublisherSociety for Industrial and Applied Mathematics Publications
Pages100-108
Number of pages9
ISBN (Electronic)9781611976236
DOIs
Publication statusPublished - 2020
Event2020 SIAM International Conference on Data Mining, SDM 2020 - Cincinnati, United States
Duration: 2020 May 72020 May 9

Publication series

NameProceedings of the 2020 SIAM International Conference on Data Mining, SDM 2020

Conference

Conference2020 SIAM International Conference on Data Mining, SDM 2020
CountryUnited States
CityCincinnati
Period20/5/720/5/9

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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  • Cite this

    Chang, B., Koh, Y., Park, D., & Kang, J. (2020). Content-aware successive point-of-interest recommendation. In C. Demeniconi, & N. Chawla (Eds.), Proceedings of the 2020 SIAM International Conference on Data Mining, SDM 2020 (pp. 100-108). (Proceedings of the 2020 SIAM International Conference on Data Mining, SDM 2020). Society for Industrial and Applied Mathematics Publications. https://doi.org/10.1137/1.9781611976236.12