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.

翻译：回归模型在推荐系统中至关重要。然而，再变换偏差问题在该领域内一直被显著忽视。尽管其他领域的许多工作已设计出有效的偏差校正方法，但它们都是模型外部的事后补救措施，在应用于现实世界的推荐系统时面临实际挑战。因此，我们提出了一种预防性范式，通过微小的模型改进，从模型内部本质性地消除偏差。具体而言，我们提出了一种新颖的TranSUN方法，采用联合偏差学习方式，在经验上更优的收敛性下提供理论保证的无偏性。该方法进一步被推广为一个新颖的通用回归模型家族，称为广义TranSUN（GTS），它不仅提供了更多的理论见解，而且作为一个通用框架，可用于灵活开发各种无偏模型。全面的实验结果表明，我们的方法在处理来自不同领域的数据时均表现出优越性，并已成功部署在两个真实的工业推荐场景中，即淘宝App（一个日活跃用户超过3亿的领先电子商务平台）首页“猜你喜欢”业务域下的商品推荐和短视频推荐场景，服务于主要的在线流量。