DecKG: Decentralized Collaborative Learning with Knowledge Graph Enhancement for POI Recommendation

TL;DR

DecKG introduces a decentralized POI recommendation framework that enhances privacy and performance by leveraging partitioned knowledge graphs and local data refinement, addressing challenges of data privacy and resource constraints.

Contribution

It proposes a novel decentralized learning framework with knowledge graph enhancement that preserves privacy and improves POI recommendation accuracy.

Findings

DecKG outperforms baseline models in recommendation accuracy.

Knowledge graph partitioning reduces local storage burden.

Client-to-client knowledge exchange improves personalization.

Abstract

Decentralized collaborative learning for Point-of-Interest (POI) recommendation has gained research interest due to its advantages in privacy preservation and efficiency, as it keeps data locally and leverages collaborative learning among clients to train models in a decentralized manner. However, since local data is often limited and insufficient for training accurate models, a common solution is integrating external knowledge as auxiliary information to enhance model performance. Nevertheless, this solution poses challenges for decentralized collaborative learning. Due to private nature of local data, identifying relevant auxiliary information specific to each user is non-trivial. Furthermore, resource-constrained local devices struggle to accommodate all auxiliary information, which places heavy burden on local storage. To fill the gap, we propose a novel decentralized collaborative learning with knowledge graph enhancement framework for POI recommendation (DecKG). Instead of directly uploading interacted items, users generate desensitized check-in data by uploading general categories of interacted items and sampling similar items from same category. The server then pretrains KG without sensitive user-item interactions and deploys relevant partitioned sub-KGs to individual users. Entities are further refined on the device, allowing client to client communication to exchange knowledge learned from local data and sub-KGs. Evaluations across two real-world datasets demonstrate DecKG's effectiveness recommendation performance.