research-article

EEDN: Enhanced Encoder-Decoder Network with Local and Global Context Learning for POI Recommendation

Authors:

Fumiyo Fukumoto,

Yoshimi Suzuki,

Dongjin YuAuthors Info & Claims

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 383 - 392

https://doi.org/10.1145/3539618.3591678

Published: 18 July 2023 Publication History

Abstract

The point-of-interest (POI) recommendation predicts users' destinations, which might be of interest to users and has attracted considerable attention as one of the major applications in location-based social networks (LBSNs). Recent work on graph-based neural networks (GNN) or matrix factorization-based (MF) approaches has resulted in better representations of users and POIs to forecast users' latent preferences. However, they still suffer from the implicit feedback and cold-start problems of check-in data, as they cannot capture both local and global graph-based relations among users (or POIs) simultaneously, and the cold-start neighbors are not handled properly during graph convolution in GNN. In this paper, we propose an enhanced encoder-decoder network (EEDN) to exploit rich latent features between users, POIs, and interactions between users and POIs for POI recommendation. The encoder of EEDN utilizes a hybrid hypergraph convolution to enhance the aggregation ability of each graph convolution step and learns to derive more robust cold-start-aware user representations. In contrast, the decoder mines local and global interactions by both graph- and sequential-based patterns for modeling implicit feedback, especially to alleviate exposure bias. Extensive experiments in three public real-world datasets demonstrate that EEDN outperforms state-of-the-art methods. Our source codes and data are released at https://github.com/WangXFng/EEDN

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Zhang XWeng HWei YWang DChen JLiang TYin Y(2025)Multivariate Hawkes Spatio-Temporal Point Process with attention for point of interest recommendationNeurocomputing10.1016/j.neucom.2024.129161619(129161)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.129161
Wang ZHöpken WJannach D(2025)A survey on point-of-interest recommendations leveraging heterogeneous dataInformation Technology & Tourism10.1007/s40558-024-00301-3Online publication date: 4-Jan-2025
https://doi.org/10.1007/s40558-024-00301-3
Liu YShen GCui CZhao ZHan XDu JZhao XKong XLarson K(2024)KDDCProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/244(2207-2215)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/244
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Index Terms

EEDN: Enhanced Encoder-Decoder Network with Local and Global Context Learning for POI Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2023

3567 pages

ISBN:9781450394086

DOI:10.1145/3539618

General Chairs:
Hsin-Hsi Chen
National Taiwan University
,
Wei-Jou (Edward) Duh
National Taiwan University
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Hen-Hsen Huang
Academia Sinica
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Program Chairs:
Makoto P. Kato
Spotify
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Josiane Mothe
Universite de Toulouse
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Barbara Poblete
University of Chile and Amazon Visiting Academic

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Published: 18 July 2023

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China Scholarship Council
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Suzuki Foundation
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SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 23 - 27, 2023

Taipei, Taiwan

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Cited By

Zhang XWeng HWei YWang DChen JLiang TYin Y(2025)Multivariate Hawkes Spatio-Temporal Point Process with attention for point of interest recommendationNeurocomputing10.1016/j.neucom.2024.129161619(129161)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.129161
Wang ZHöpken WJannach D(2025)A survey on point-of-interest recommendations leveraging heterogeneous dataInformation Technology & Tourism10.1007/s40558-024-00301-3Online publication date: 4-Jan-2025
https://doi.org/10.1007/s40558-024-00301-3
Liu YShen GCui CZhao ZHan XDu JZhao XKong XLarson K(2024)KDDCProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/244(2207-2215)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/244
Sun KLi CQian T(2024)City Matters! A Dual-Target Cross-City Sequential POI Recommendation ModelACM Transactions on Information Systems10.1145/366428442:6(1-27)Online publication date: 19-Aug-2024
https://dl.acm.org/doi/10.1145/3664284
Wang SXie BDing LGao XChen JXiang Y(2024)SeCor: Aligning Semantic and Collaborative Representations by Large Language Models for Next-Point-of-Interest RecommendationsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688124(1-11)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688124
Feng SMeng FChen LShang SOng YBaeza-Yates RBonchi F(2024)ROTAN: A Rotation-based Temporal Attention Network for Time-Specific Next POI RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671809(759-770)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671809
Wang XFukumoto FCui JSuzuki YYu DHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)NFARec: A Negative Feedback-Aware Recommender ModelProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657809(935-945)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657809
Wang XFukumoto FCui JSuzuki YLi JYu DHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)CaDRec: Contextualized and Debiased Recommender ModelProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657799(405-415)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657799
Xu YCong GZhu LCui LChua TNgo CKa-Wei Lee RKumar RLauw H(2024)MMPOI: A Multi-Modal Content-Aware Framework for POI RecommendationsProceedings of the ACM Web Conference 202410.1145/3589334.3645449(3454-3463)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645449
Zuo CZhang XYan LZhang Z(2024)GUGEN: Global User Graph Enhanced Network for Next POI RecommendationIEEE Transactions on Mobile Computing10.1109/TMC.2024.345510723:12(14975-14986)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1109/TMC.2024.3455107
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