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Industrial recommender systems face the challenge of operating innon-stationary environments, where data distribution shifts arise from evolvinguser behaviors over time. To tackle this challenge, a common approach is toperiodically re-train or incrementally update deployed deep models with newlyobserved data, resulting in a continual training process. However, theconventional learning paradigm of neural networks relies on iterativegradient-based updates with a small learning rate, making it slow for largerecommendation models to adapt. In this paper, we introduce ReLoop2, aself-correcting learning loop that facilitates fast model adaptation in onlinerecommender systems through responsive error compensation. Inspired by theslow-fast complementary learning system observed in human brains, we propose anerror memory module that directly stores error samples from incoming datastreams. These stored samples are subsequently leveraged to compensate formodel prediction errors during testing, particularly under distribution shifts.The error memory module is designed with fast access capabilities and undergoescontinual refreshing with newly observed data samples during the model servingphase to support fast model adaptation. We evaluate the effectiveness ofReLoop2 on three open benchmark datasets as well as a real-world productiondataset. The results demonstrate the potential of ReLoop2 in enhancing theresponsiveness and adaptiveness of recommender systems operating innon-stationary environments.

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