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

by   Jieming Zhu, et al.

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.


Non-Stationary Latent Bandits

Users of recommender systems often behave in a non-stationary fashion, d...

KuaiRec: A Fully-observed Dataset for Recommender Systems

Recommender systems are usually developed and evaluated on the historica...

Disentangled Causal Embedding With Contrastive Learning For Recommender System

Recommender systems usually rely on observed user interaction data to bu...

Hop Sampling: A Simple Regularized Graph Learning for Non-Stationary Environments

Graph representation learning is gaining popularity in a wide range of a...

Learning Fast and Slow for Online Time Series Forecasting

The fast adaptation capability of deep neural networks in non-stationary...

Streaming CTR Prediction: Rethinking Recommendation Task for Real-World Streaming Data

The Click-Through Rate (CTR) prediction task is critical in industrial r...

Discrete Key-Value Bottleneck

Deep neural networks perform well on prediction and classification tasks...

Please sign up or login with your details

Forgot password? Click here to reset