Decentralized Collaborative Learning with Adaptive Reference Data for On-Device POI Recommendation
CoRR(2024)
摘要
In Location-based Social Networks, Point-of-Interest (POI) recommendation
helps users discover interesting places. There is a trend to move from the
cloud-based model to on-device recommendations for privacy protection and
reduced server reliance. Due to the scarcity of local user-item interactions on
individual devices, solely relying on local instances is not adequate.
Collaborative Learning (CL) emerges to promote model sharing among users, where
reference data is an intermediary that allows users to exchange their soft
decisions without directly sharing their private data or parameters, ensuring
privacy and benefiting from collaboration. However, existing CL-based
recommendations typically use a single reference for all users. Reference data
valuable for one user might be harmful to another, given diverse user
preferences. Users may not offer meaningful soft decisions on items outside
their interest scope. Consequently, using the same reference data for all
collaborations can impede knowledge exchange and lead to sub-optimal
performance. To address this gap, we introduce the Decentralized Collaborative
Learning with Adaptive Reference Data (DARD) framework, which crafts adaptive
reference data for effective user collaboration. It first generates a
desensitized public reference data pool with transformation and probability
data generation methods. For each user, the selection of adaptive reference
data is executed in parallel by training loss tracking and influence function.
Local models are trained with individual private data and collaboratively with
the geographical and semantic neighbors. During the collaboration between two
users, they exchange soft decisions based on a combined set of their adaptive
reference data. Our evaluations across two real-world datasets highlight DARD's
superiority in recommendation performance and addressing the scarcity of
available reference data.
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