A Probabilistic Framework for Temporal Distribution Generalization in Industry-Scale Recommender Systems

TL;DR

This paper introduces ELBO_TDS, a probabilistic framework designed to improve temporal distribution generalization in industry-scale recommender systems, addressing distribution shifts with a causal modeling approach and data augmentation.

Contribution

The paper presents a novel probabilistic framework, ELBO_TDS, that models temporal shifts using causal graphs and variational objectives, enhancing long-term recommendation accuracy.

Findings

Achieves a 2.33% uplift in GMV per user.

Successfully deployed in Shopee Product Search.

Outperforms existing methods in temporal generalization.

Abstract

Temporal distribution shift (TDS) erodes the long-term accuracy of recommender systems, yet industrial practice still relies on periodic incremental training, which struggles to capture both stable and transient patterns. Existing approaches such as invariant learning and self-supervised learning offer partial solutions but often suffer from unstable temporal generalization, representation collapse, or inefficient data utilization. To address these limitations, we propose ELBO$_\text{TDS}$, a probabilistic framework that integrates seamlessly into industry-scale incremental learning pipelines. First, we identify key shifting factors through statistical analysis of real-world production data and design a simple yet effective data augmentation strategy that resamples these time-varying factors to extend the training support. Second, to harness the benefits of this extended distribution while preventing representation collapse, we model the temporal recommendation scenario using a causal graph and derive a self-supervised variational objective, ELBO$_\text{TDS}$, grounded in the causal structure. Extensive experiments supported by both theoretical and empirical analysis demonstrate that our method achieves superior temporal generalization, yielding a 2.33\% uplift in GMV per user and has been successfully deployed in Shopee Product Search. Code is available at https://github.com/FuCongResearchSquad/ELBO4TDS.