ConAlign: Conditional Alignment Framework for Balancing Biased and Unbiased Recommendation
2026-07-27 • Information Retrieval
Information Retrieval
AI summaryⓘ
The authors address a problem where recommendation systems tend to show users only a narrow range of interests due to bias in training data, which harms long-term engagement. They introduce ConAlign, a new method that carefully balances biased and unbiased data to improve recommendations without needing heavy computation or losing accuracy. ConAlign works by selectively combining information from biased and unbiased models, allowing it to adapt quickly and maintain real-time performance. The authors tested their approach on real-world data and in a large-scale live setting, showing better user engagement and more diverse recommendations with little added delay.
Recommender SystemsBiasFilter BubblesDebiasingConditional AlignmentStreaming AdaptationA/B TestingUser EngagementInterest Diversity
Authors
Jingcheng Zhang, Yihan Wang, Qi Song, Liyin Hong
Abstract
Industry recommender systems trained on observational data suffer from various biases that create filter bubbles, causing user interests to collapse into narrow categories and severely degrading long-term engagement. While utilizing unbiased uniform data for debiasing has shown promise, existing methods remain impractical for industrial deployment due to limitations such as neglect of factual (biased) recommendation performance and the substantial computational overhead. To overcome these limitations, we propose ConAlign (Conditional Alignment Framework), a conditional debiasing approach for industrial deployment. The key innovation of ConAlign lies in a discrete gating-based conditional alignment mechanism that selectively transfers knowledge from the biased tower to the unbiased tower. Following a selective intervention paradigm rather than universal correction, it seamlessly balances factual accuracy and unbiased preference estimation while supporting real-time streaming adaptation. To the best of our knowledge, ConAlign is the first streaming debiasing recommendation framework successfully deployed in a large-scale industrial recommendation system that utilizes a small fraction of unbiased random traffic for debiasing. Extensive offline experiments on three real-world datasets rigorously validate the effectiveness of our proposed framework. Furthermore, large-scale online A/B testing on Kuaishou demonstrates significant improvements in long-term user engagement and interest diversity, with negligible latency overhead.