提出新方法从根源消除推荐系统回归模型的重转换偏差。
TranSUN: A Preemptive Paradigm to Eradicate Retransformation Bias Intrinsically from Regression Models in Recommender Systems
- 通过联合学习机制在模型内部主动消除偏差
- 在多个真实场景中验证效果，收敛更快更准
- 适合需要高精度推荐的工业级系统部署
回归模型在推荐系统中至关重要，但重转换偏差问题长期被忽视。现有修正方法均为事后补救，难以应用于真实推荐系统。为此，本文提出一种预置性范式，通过微调模型结构内在消除偏差。提出新型TranSUN方法，采用联合偏差学习策略，在实证中实现理论保证的无偏性与优越收敛性能。进一步推广为通用回归模型族——广义TranSUN（GTS），提供更深入理论理解，并可灵活构建各类无偏模型。大量实验表明，该方法在多领域数据上均表现优异，已在淘宝首页两个真实业务场景（商品与短视频推荐）成功部署，服务日活超3亿用户的主流量，显著提升推荐效果。
原文摘要 · Abstract (English)
Regression models are crucial in recommender systems. However, retransformation bias problem has been conspicuously neglected within the community. While many works in other fields have devised effective bias correction methods, all of them are post-hoc cures externally to the model, facing practical challenges when applied to real-world recommender systems. Hence, we propose a preemptive paradigm to eradicate the bias intrinsically from the models via minor model refinement. Specifically, a novel TranSUN method is proposed with a joint bias learning manner to offer theoretically guaranteed unbiasedness under empirical superior convergence. It is further generalized into a novel generic regression model family, termed Generalized TranSUN (GTS), which not only offers more theoretical insights but also serves as a generic framework for flexibly developing various bias-free models. Comprehensive experimental results demonstrate the superiority of our methods across data from various domains, which have been successfully deployed in two real-world industrial recommendation scenarios, i.e. product and short video recommendation scenarios in Guess What You Like business domain in the homepage of Taobao App (a leading e-commerce platform with DAU > 300M), to serve the major online traffic.
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