Intelligent Model Update Strategy for Sequential Recommendation

Abstract

Modern online platforms are increasingly employing recommendation systems to address information overload and improve user engagement. There is an evolving paradigm in this research field that recommendation network learning occurs both on the cloud and on edges with knowledge transfer in between (i.e., edge-cloud collaboration). Recent works push this field further by enabling edge-specific context-aware adaptivity, where model parameters are updated in real-time based on incoming on-edge data. However, we argue that frequent data exchanges between the cloud and edges often lead to inefficiency and waste of communication/computation resources, as considerable parameter updates might be redundant. To investigate this problem, we introduce Intelligent Edge-Cloud Parameter Request Model, abbreviated as IntellectReq. IntellectReq is designed to operate on edge, evaluating the cost-benefit landscape of parameter requests with minimal computation and communication overhead. We formulate this as a novel learning task, aimed at the detection of out-of-distribution data, thereby fine-tuning adaptive communication strategies. Further, we employ statistical mapping techniques to convert real-time user behavior into a normal distribution, thereby employing multi-sample outputs to quantify the model's uncertainty and thus its generalization capabilities. Rigorous empirical validation on four widely-adopted benchmarks evaluates our approach, evidencing a marked improvement in the efficiency and generalizability of edge-cloud collaborative and dynamic recommendation systems.

Published on WWW'24(Oral): Proceedings of the ACM on Web Conference 2024 (pp. 3117-3128)

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• Session-based Recommendation with Graph Neural Networks
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• Session-aware Recommendation: A Surprising Quest for the State-of-the-art
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• GenerSpeech: Towards Style Transfer for Generalizable Out-Of-Domain Text-to-Speech
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Cited by in corpus (3)

• Collaboration of Large Language Models and Small Recommendation Models for Device-Cloud Recommendation
• Adaptive political surveys and GPT-4: Tackling the cold start problem with simulated user interactions
• Preliminary Evaluation of the Test-Time Training Layers in Recommendation System (Student Abstract)