Decentralized Collaborative Learning Framework for Next POI Recommendation

• URL: http://arxiv.org/abs/2204.06516v1
• Date: Wed, 30 Mar 2022 11:00:11 GMT
• Title: Decentralized Collaborative Learning Framework for Next POI Recommendation
• Authors: Jing Long, Tong Chen, Nguyen Quoc Viet Hung, Hongzhi Yin
• Abstract summary: 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.
• Score: 39.65626819903099
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Next Point-of-Interest (POI) recommendation has become an indispensable functionality in Location-based Social Networks (LBSNs) due to its effectiveness in helping people decide the next POI to visit. However, 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. Although there have been several on-device frameworks for privacy-preserving POI recommendations, they are still resource-intensive when it comes to storage and computation, and show limited robustness to the high sparsity of user-POI interactions. On this basis, 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. DCLR significantly reduces the local models' dependence on the cloud for training, and can be used to expand arbitrary centralized recommendation models. To counteract the sparsity of on-device user data when learning each local model, we design two self-supervision signals to pretrain the POI representations on the server with geographical and categorical correlations of POIs. To facilitate collaborative learning, we innovatively propose to incorporate knowledge from either geographically or semantically similar users into each local model with attentive aggregation and mutual information maximization. The collaborative learning process makes use of communications between devices while requiring only minor engagement from the central server for identifying user groups, and is compatible with common privacy preservation mechanisms like differential privacy.

Related papers

• DecKG: Decentralized Collaborative Learning with Knowledge Graph Enhancement for POI Recommendation [33.42817477508175]
  Decentralized collaborative learning for Point-of-Interest (POI) recommendation has gained research interest. We propose a novel decentralized collaborative learning with knowledge graph enhancement framework for POI recommendation (DecKG)
  arXiv  Detail & Related papers  (2024-10-14T03:37:47Z)
• Diffusion-Based Cloud-Edge-Device Collaborative Learning for Next POI Recommendations [39.29562719543681]
  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)
  arXiv  Detail & Related papers  (2024-05-22T16:41:23Z)
• 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)
• DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training [84.81043932706375]
  We propose a novel personalized federated learning framework in a decentralized (peer-to-peer) communication protocol named Dis-PFL. Dis-PFL employs personalized sparse masks to customize sparse local models on the edge. We demonstrate that our method can easily adapt to heterogeneous local clients with varying computation complexities.
  arXiv  Detail & Related papers  (2022-06-01T02:20:57Z)
• 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)
• FedPOIRec: Privacy Preserving Federated POI Recommendation with Social Influence [0.0]
  We present FedPOIRec, a privacy preserving federated learning approach enhanced with features from users' social circles for top-$N$ POI recommendations. First, the FedPOIRec framework is built on the principle that local data never leave the owner's device, while the local updates are blindly aggregated by a parameter server. We propose a privacy preserving protocol for integrating the preferences of a user's friends after the federated computation, by exploiting the properties of the CKKS fully homomorphic encryption scheme.
  arXiv  Detail & Related papers  (2021-12-21T12:11:39Z)
• How to Put Users in Control of their Data in Federated Top-N Recommendation with Learning to Rank [16.256897977543982]
  We present FPL, an architecture in which users collaborate in training a central factorization model while controlling the amount of sensitive data leaving their devices. The proposed approach implements pair-wise learning-to-rank optimization by following the Federated Learning principles.
  arXiv  Detail & Related papers  (2020-08-17T10:13:15Z)
• Decentralised Learning from Independent Multi-Domain Labels for Person Re-Identification [69.29602103582782]
  Deep learning has been successful for many computer vision tasks due to the availability of shared and centralised large-scale training data. However, increasing awareness of privacy concerns poses new challenges to deep learning, especially for person re-identification (Re-ID) We propose a novel paradigm called Federated Person Re-Identification (FedReID) to construct a generalisable global model (a central server) by simultaneously learning with multiple privacy-preserved local models (local clients) This client-server collaborative learning process is iteratively performed under privacy control, enabling FedReID to realise decentralised learning without sharing distributed data nor collecting any
  arXiv  Detail & Related papers  (2020-06-07T13:32:33Z)
• Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem [55.21502268698577]
  This work investigates the task of unsupervised model personalization, adapted to continually evolving, unlabeled local user images. We provide a novel Dual User-Adaptation framework (DUA) to explore the problem. This framework flexibly disentangles user-adaptation into model personalization on the server and local data regularization on the user device.
  arXiv  Detail & Related papers  (2020-03-30T09:35:12Z)

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.