research-article

PREFER: Point-of-interest REcommendation with efficiency and privacy-preservation via Federated Edge leaRning

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Nong XiaoAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 5, Issue 1

Article No.: 13, Pages 1 - 25

https://doi.org/10.1145/3448099

Published: 30 March 2021 Publication History

Abstract

Point-of-Interest (POI) recommendation is significant in location-based social networks to help users discover new locations of interest. Previous studies on such recommendation mainly adopted a centralized learning framework where check-in data were uploaded, trained and predicted centrally in the cloud. However, such a framework suffers from privacy risks caused by check-in data exposure and fails to meet real-time recommendation needs when the data volume is huge and communication is blocked in crowded places. In this paper, we propose PREFER, an edge-accelerated federated learning framework for POI recommendation. It decouples the recommendation into two parts. Firstly, to protect privacy, users train local recommendation models and share multi-dimensional user-independent parameters instead of check-in data. Secondly, to improve recommendation efficiency, we aggregate these distributed parameters on edge servers in proximity to users (such as base stations) instead of remote cloud servers. We implement the PREFER prototype and evaluate its performance using two real-world datasets and two POI recommendation models. Extensive experiments demonstrate that PREFER strengthens privacy protection and improves efficiency with little sacrifice to recommendation quality compared to centralized learning. It achieves the best quality and efficiency and is more compatible with increasingly sophisticated POI recommendation models compared to other state-of-the-art privacy-preserving baselines.

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Cui SLiu ZLiu FYe YZhang M(2025)Integrating conceptual and visual representations with domain expertise for scalable visual plagiarism detectionExpert Systems with Applications10.1016/j.eswa.2024.125974267(125974)Online publication date: Apr-2025
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Ayadi MMezni HElmannai HAlkanhel R(2025)Privacy-preserving cross-network service recommendation via federated learning of unified user representationsData & Knowledge Engineering10.1016/j.datak.2025.102422(102422)Online publication date: Mar-2025
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Index Terms

PREFER: Point-of-interest REcommendation with efficiency and privacy-preservation via Federated Edge leaRning
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing systems and tools
  2. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing systems and tools
2. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Social aspects of security and privacy

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 5, Issue 1

March 2021

1272 pages

EISSN:2474-9567

DOI:10.1145/3459088

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Published: 30 March 2021

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Cui SLiu ZLiu FYe YZhang M(2025)Integrating conceptual and visual representations with domain expertise for scalable visual plagiarism detectionExpert Systems with Applications10.1016/j.eswa.2024.125974267(125974)Online publication date: Apr-2025
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https://doi.org/10.1016/j.datak.2025.102422
Acharya MMohbey K(2024)Recency-based spatio-temporal similarity exploration for POI recommendation in location-based social networksFrontiers in Sustainable Cities10.3389/frsc.2024.13316426Online publication date: 24-Apr-2024
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