Model-agnostic decentralized collaborative learning for on-device POI recommendation

Jing LONG, Tong CHEN, Quoc Viet Hung NGUYEN, Guandong XU, Kai ZHENG, Hongzhi YIN

Research output: Chapter in Book/Report/Conference proceedingChapters

7 Citations (Scopus)


As an indispensable personalized service in Location-based Social Networks (LBSNs), the next Point-of-Interest (POI) recommendation aims to help people discover attractive and interesting places. Currently, most POI recommenders are based on the conventional centralized paradigm that heavily relies on the cloud to train the recommendation models with large volumes of collected users' sensitive check-in data. Although a few recent works have explored on-device frameworks for resilient and privacy-preserving POI recommendations, they invariably hold the assumption of model homogeneity for parameters/gradients aggregation and collaboration. However, users' mobile devices in the real world have various hardware configurations (e.g., compute resources), leading to heterogeneous on-device models with different architectures and sizes. In light of this, We propose a novel on-device POI recommendation framework, namely Model-Agnostic Collaborative learning for on-device POI recommendation (MAC), allowing users to customize their own model structures (e.g., dimension & number of hidden layers). To counteract the sparsity of on-device user data, we propose to pre-select neighbors for collaboration based on physical distances, category-level preferences, and social networks. To assimilate knowledge from the above-selected neighbors in an efficient and secure way, we adopt the knowledge distillation framework with mutual information maximization. Instead of sharing sensitive models/gradients, clients in MAC only share their soft decisions on a preloaded reference dataset. To filter out low-quality neighbors, we propose two sampling strategies, performance-triggered sampling and similarity-based sampling, to speed up the training process and obtain optimal recommenders. In addition, we design two novel approaches to generate more effective reference datasets while protecting users' privacy. Extensive experiments on two datasets have shown the superiority of MAC over advanced baselines. Copyright © 2023 held by the owner/author(s). Publication rights licensed to ACM.

Original languageEnglish
Title of host publicationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
Place of PublicationNew York
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450394086
Publication statusPublished - Jul 2023


Long, J., Chen, T., Nguyen, Q. V. H., Xu, G., Zheng, K., & Yin, H. (2023). Model-agnostic decentralized collaborative learning for on-device POI recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 423-432). Association for Computing Machinery.


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