ReLoop2: Building Self-Adaptive Recommendation Models via Responsive Error Compensation Loop

TL;DR

ReLoop2 introduces a self-adaptive learning loop with an error memory module that enables recommender systems to quickly adapt to changing data distributions by directly compensating for prediction errors during testing.

Contribution

The paper proposes ReLoop2, a novel self-correcting learning framework that uses an error memory module for fast adaptation in non-stationary recommender systems.

Findings

Improves model responsiveness to distribution shifts

Enhances recommendation accuracy in dynamic environments

Demonstrates effectiveness on benchmark and real-world datasets

Abstract

Industrial recommender systems face the challenge of operating in non-stationary environments, where data distribution shifts arise from evolving user behaviors over time. To tackle this challenge, a common approach is to periodically re-train or incrementally update deployed deep models with newly observed data, resulting in a continual training process. However, the conventional learning paradigm of neural networks relies on iterative gradient-based updates with a small learning rate, making it slow for large recommendation models to adapt. In this paper, we introduce ReLoop2, a self-correcting learning loop that facilitates fast model adaptation in online recommender systems through responsive error compensation. Inspired by the slow-fast complementary learning system observed in human brains, we propose an error memory module that directly stores error samples from incoming data streams. These stored samples are subsequently leveraged to compensate for model prediction errors during testing, particularly under distribution shifts. The error memory module is designed with fast access capabilities and undergoes continual refreshing with newly observed data samples during the model serving phase to support fast model adaptation. We evaluate the effectiveness of ReLoop2 on three open benchmark datasets as well as a real-world production dataset. The results demonstrate the potential of ReLoop2 in enhancing the responsiveness and adaptiveness of recommender systems operating in non-stationary environments.