research-article

Fine-Grained Preference-Aware Personalized Federated POI Recommendation with Data Sparsity

Authors:

Dongxiao YuAuthors Info & Claims

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

Pages 413 - 422

https://doi.org/10.1145/3539618.3591688

Published: 18 July 2023 Publication History

Abstract

With the raised privacy concerns and rigorous data regulations, federated learning has become a hot collaborative learning paradigm for the recommendation model without sharing the highly sensitive POI data. However, the time-sensitive, heterogeneous, and limited POI records seriously restrict the development of federated POI recommendation. To this end, in this paper, we design the fine-grained preference-aware personalized federated POI recommendation framework, namely PrefFedPOI, under extremely sparse historical trajectories to address the above challenges. In details, PrefFedPOI extracts the fine-grained preference of current time slot by combining historical recent preferences and periodic preferences within each local client. Due to the extreme lack of POI data in some time slots, a data amount aware selective strategy is designed for model parameters uploading. Moreover, a performance enhanced clustering mechanism with reinforcement learning is proposed to capture the preference relatedness among all clients to encourage the positive knowledge sharing. Furthermore, a clustering teacher network is designed for improving efficiency by clustering guidance. Extensive experiments are conducted on two diverse real-world datasets to demonstrate the effectiveness of proposed PrefFedPOI comparing with state-of-the-arts. In particular, personalized PrefFedPOI can achieve 7% accuracy improvement on average among data-sparsity clients.

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https://dl.acm.org/doi/10.1145/3708982
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
Zhang CLong GGuo HFang XSong YLiu ZZhou GZhang ZLiu YYang BLarson K(2024)Federated adaptation for foundation model-based recommendationsProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/603(5453-5461)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/603
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Index Terms

Fine-Grained Preference-Aware Personalized Federated POI Recommendation with Data Sparsity
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Location based services

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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
,
Hen-Hsen Huang
Academia Sinica
,
Program Chairs:
Makoto P. Kato
Spotify
,
Josiane Mothe
Universite de Toulouse
,
Barbara Poblete
University of Chile and Amazon Visiting Academic

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Publication History

Published: 18 July 2023

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National Natural Science Foundation of China
Shandong Provincial Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
the National Key Research and Development Program of China

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SIGIR '23

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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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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Ali WZhou XShao J(2025)Privacy-preserved and Responsible Recommenders: From Conventional Defense to Federated Learning and BlockchainACM Computing Surveys10.1145/370898257:5(1-35)Online publication date: 9-Jan-2025
https://dl.acm.org/doi/10.1145/3708982
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
Zhang CLong GGuo HFang XSong YLiu ZZhou GZhang ZLiu YYang BLarson K(2024)Federated adaptation for foundation model-based recommendationsProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/603(5453-5461)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/603
He XLiu YHe WXing XLu XLiu Y(2024)TCKT: Tree-Based Cross-domain Knowledge Transfer for Next POI Cold-Start RecommendationACM Transactions on Information Systems10.1145/3709137Online publication date: 24-Dec-2024
https://dl.acm.org/doi/10.1145/3709137
Wang LZhou HBao YYan XShen GKong X(2024)Horizontal Federated Recommender System: A SurveyACM Computing Surveys10.1145/365616556:9(1-42)Online publication date: 8-May-2024
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Xu SGuan DSerra ESpezzano F(2024)CrossPred: A Cross-City Mobility Prediction Framework for Long-Distance Travelers via POI Feature MatchingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679893(4148-4152)Online publication date: 21-Oct-2024
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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
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Guo PWang QJiang JWu SXiang HJi XXue S(2024)Mining Relational Similarity in Social Networks for Enhanced Recommendations2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA)10.1109/ISPA63168.2024.00020(90-97)Online publication date: 30-Oct-2024
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Li MZhang XYing HLi YHan XYu D(2023)Data Quality Aware Hierarchical Federated Reinforcement Learning Framework for Dynamic Treatment Regimes2023 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM58522.2023.00131(1103-1108)Online publication date: 1-Dec-2023
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