Diffusion-Based Cloud-Edge-Device Collaborative Learning for Next POI Recommendations

• URL: http://arxiv.org/abs/2405.13811v1
• Date: Wed, 22 May 2024 16:41:23 GMT
• Title: Diffusion-Based Cloud-Edge-Device Collaborative Learning for Next POI Recommendations
• Authors: Jing Long, Guanhua Ye, Tong Chen, Yang Wang, Meng Wang, Hongzhi Yin,
• Abstract summary: Next Point-of-Interest (POI) recommendations leverage historical check-in data to predict users' next POIs to visit. Traditional centralized deep neural networks (DNNs) offer impressive POI recommendation performance but face challenges due to privacy concerns and limited timeliness. On-device POI recommendations have been introduced, utilizing federated learning (FL) and decentralized approaches to ensure privacy and recommendation timeliness. This paper introduces a novel collaborative learning framework, Diffusion-Based Cloud-Edge-Device Collaborative Learning for Next POI Recommendations (DCPR)
• Score: 39.29562719543681
• License:
• Abstract: The rapid expansion of Location-Based Social Networks (LBSNs) has highlighted the importance of effective next Point-of-Interest (POI) recommendations, which leverage historical check-in data to predict users' next POIs to visit. Traditional centralized deep neural networks (DNNs) offer impressive POI recommendation performance but face challenges due to privacy concerns and limited timeliness. In response, on-device POI recommendations have been introduced, utilizing federated learning (FL) and decentralized approaches to ensure privacy and recommendation timeliness. However, these methods often suffer from computational strain on devices and struggle to adapt to new users and regions. This paper introduces a novel collaborative learning framework, Diffusion-Based Cloud-Edge-Device Collaborative Learning for Next POI Recommendations (DCPR), leveraging the diffusion model known for its success across various domains. DCPR operates with a cloud-edge-device architecture to offer region-specific and highly personalized POI recommendations while reducing on-device computational burdens. DCPR minimizes on-device computational demands through a unique blend of global and local learning processes. Our evaluation with two real-world datasets demonstrates DCPR's superior performance in recommendation accuracy, efficiency, and adaptability to new users and regions, marking a significant step forward in on-device POI recommendation technology.

Related papers

• Efficient and Robust Regularized Federated Recommendation [52.24782464815489]
  The recommender system (RSRS) addresses both user preference and privacy concerns. We propose a novel method that incorporates non-uniform gradient descent to improve communication efficiency. RFRecF's superior robustness compared to diverse baselines.
  arXiv  Detail & Related papers  (2024-11-03T12:10:20Z)
• Bayes-enhanced Multi-view Attention Networks for Robust POI Recommendation [81.4999547454189]
  Existing works assume the available POI check-ins reported by users are the ground-truth depiction of user behaviors. In real application scenarios, the check-in data can be rather unreliable due to both subjective and objective causes. We propose a Bayes-enhanced Multi-view Attention Network to address the uncertainty factors of the user check-ins.
  arXiv  Detail & Related papers  (2023-11-01T12:47:38Z)
• Memory efficient location recommendation through proximity-aware representation [8.505840656442217]
  We introduce a Proximity-aware based region representation for Sequential Recommendation (PASR) We tackle the sparsity issue through a novel loss function employing importance sampling, which emphasizes informative negative samples during optimization. We conducted evaluations using three real-world Location-Based Social Networking (LBSN) datasets, demonstrating that PASR surpasses state-of-the-art sequential location recommendation methods.
  arXiv  Detail & Related papers  (2023-10-10T09:53:07Z)
• Semi-decentralized Federated Ego Graph Learning for Recommendation [58.21409625065663]
  We propose a semi-decentralized federated ego graph learning framework for on-device recommendations, named SemiDFEGL. The proposed framework is model-agnostic, meaning that it could be seamlessly integrated with existing graph neural network-based recommendation methods and privacy protection techniques.
  arXiv  Detail & Related papers  (2023-02-10T03:57:45Z)
• Self-supervised Graph-based Point-of-interest Recommendation [66.58064122520747]
  Next Point-of-Interest (POI) recommendation has become a prominent component in location-based e-commerce. We propose a Self-supervised Graph-enhanced POI Recommender (S2GRec) for next POI recommendation. In particular, we devise a novel Graph-enhanced Self-attentive layer to incorporate the collaborative signals from both global transition graph and local trajectory graphs.
  arXiv  Detail & Related papers  (2022-10-22T17:29:34Z)
• Empowering Next POI Recommendation with Multi-Relational Modeling [27.27372864066066]
  Location-based social networks (LBSNs) offer large-scale individual-level location-related activities and experiences. Next point-of-interest (POI) recommendation is one of the most important tasks in LBSNs. We propose a novel framework, MEMO, which effectively utilizes the heterogeneous relations with a multi-network representation learning module.
  arXiv  Detail & Related papers  (2022-04-24T21:51:29Z)
• Decentralized Collaborative Learning Framework for Next POI Recommendation [39.65626819903099]
  Next Point-of-Interest (POI) recommendation has become an indispensable functionality in Location-based Social Networks (LBSNs) accurate recommendation requires a vast amount of historical check-in data, thus threatening user privacy as the location-sensitive data needs to be handled by cloud servers. We propose a novel decentralized collaborative learning framework for POI recommendation (DCLR), which allows users to train their personalized models locally in a collaborative manner.
  arXiv  Detail & Related papers  (2022-03-30T11:00:11Z)
• Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Caching [91.50631418179331]
  A privacy-preserving distributed deep policy gradient (P2D3PG) is proposed to maximize the cache hit rates of devices in the MEC networks. We convert the distributed optimizations into model-free Markov decision process problems and then introduce a privacy-preserving federated learning method for popularity prediction.
  arXiv  Detail & Related papers  (2021-10-20T02:48:27Z)
• STP-UDGAT: Spatial-Temporal-Preference User Dimensional Graph Attention Network for Next POI Recommendation [22.705788963791445]
  Next Point-of-Interest (POI) recommendation is a longstanding problem across the domains of Location-Based Social Networks (LBSN) and transportation. Recent Recurrent Neural Network (RNN) based approaches learn POI-POI relationships in a local view based on independent user visit sequences. We propose a novel explore-exploit model that concurrently exploits personalized user preferences and explores new POIs in global spatial-temporal-preference neighbourhoods.
  arXiv  Detail & Related papers  (2020-10-06T04:03:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.