MBD: A Model-Based Debiasing Framework Across User, Content, and Model Dimensions

2026-03-15

AI Summary

This work addresses the misalignment between value models and users’ true preferences in recommender systems, which often arises from heterogeneous biases across user behavior, content, and other multi-source signals. To mitigate this, we propose a lightweight debiasing framework that is seamlessly embedded within existing MTML (Multi-Task Multi-Layer) ranking models. Our approach enables unified modeling across user, content, and model dimensions by constructing conditional distributions over partial feature sets and jointly estimating mean and variance within each group context. This yields controllable unbiased representations—such as percentiles or z-scores—that can be flexibly adapted to various definitions of “unbiasedness.” Implemented as an integrated branch, the framework requires no additional serving infrastructure while significantly improving model alignment with user intent and enhancing ecosystem stability.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Data Mining & Knowledge Management: Recommender Systems
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Modern recommendation systems rank candidates by aggregating multiple behavioral signals through a value model. However, many commonly used signals are inherently affected by heterogeneous biases. For example, watch time naturally favors long-form content, loop rate favors short - form content, and comment probability favors videos over images. Such biases introduce two critical issues: (1) value model scores may be systematically misaligned with users' relative preferences - for instance, a seemingly low absolute like probability may represent exceptionally strong interest for a user who rarely engages; and (2) changes in value modeling rules can trigger abrupt and undesirable ecosystem shifts. In this work, we ask a fundamental question: can biased behavioral signals be systematically transformed into unbiased signals, under a user - defined notion of ``unbiasedness'', that are both personalized and adaptive? We propose a general, model-based debiasing (MBD) framework that addresses this challenge by augmenting it with distributional modeling. By conditioning on a flexible subset of features (partial feature set), we explicitly estimate the contextual mean and variance of the engagement distribution for arbitrary cohorts (e.g., specific video lengths or user regions) directly alongside the main prediction. This integration allows the framework to convert biased raw signals into unbiased representations, enabling the construction of higher-level, calibrated signals (such as percentiles or z - scores) suitable for the value model. Importantly, the definition of unbiasedness is flexible and controllable, allowing the system to adapt to different personalization objectives and modeling preferences. Crucially, this is implemented as a lightweight, built-in branch of the existing MTML ranking model, requiring no separate serving infrastructure.

Problem

• bias
• recommendation systems
• behavioral signals
• unbiasedness
• value model

Innovation

• model-based debiasing
• distributional modeling
• calibrated signals
• personalized unbiasedness
• MTML ranking