Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation Systems

• URL: http://arxiv.org/abs/2209.01143v1
• Date: Fri, 2 Sep 2022 15:55:31 GMT
• Title: Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation Systems
• Authors: Mao Ye, Ruichen Jiang, Haoxiang Wang, Dhruv Choudhary, Xiaocong Du, Bhargav Bhushanam, Aryan Mokhtari, Arun Kejariwal, Qiang Liu
• Abstract summary: We learn a meta future gradient generator that forecasts the gradient information of the future data distribution for training. Compared with Batch Update, our theory suggests that the proposed algorithm achieves smaller temporal domain generalization error.
• Score: 30.88268793277078
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One of the key challenges of learning an online recommendation model is the temporal domain shift, which causes the mismatch between the training and testing data distribution and hence domain generalization error. To overcome, we propose to learn a meta future gradient generator that forecasts the gradient information of the future data distribution for training so that the recommendation model can be trained as if we were able to look ahead at the future of its deployment. Compared with Batch Update, a widely used paradigm, our theory suggests that the proposed algorithm achieves smaller temporal domain generalization error measured by a gradient variation term in a local regret. We demonstrate the empirical advantage by comparing with various representative baselines.

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