解决推荐系统中位置与流行度偏差问题，提升推荐相关性。
Unbiased Recommender Systems with Implicit Feedback
- 针对排序与协同过滤模型，设计去偏学习方法。
- 有效降低高排名项的虚假点击优势，提升长尾项目曝光。
- 适合关注公平性与个性化推荐的研究者与工程师。
推荐系统通常依赖隐式反馈（如点击）来推断用户偏好，但此类数据天然存在多种偏差，包括位置偏差和流行度偏差。位置偏差表现为排名靠前的项目即使不相关也获得更多交互；流行度偏差则强化热门项目的曝光，导致冷门但相关的项目被忽视。直接基于此类数据训练无法捕捉真实用户偏好，影响推荐效果。本文聚焦于缓解学习排序系统中的位置偏差以及协同过滤与基于图神经网络的社会推荐系统中的流行度偏差。所提出的方法克服了现有去偏技术的局限，使推荐更贴合用户真实偏好，实现更高相关性与个性化。
原文摘要 · Abstract (English)
Recommender systems typically rely on implicit feedback (e.g., clicks) to infer user preferences. However, such data is inherently prone to various biases, including position bias and popularity bias. Position bias occurs when higher-ranked items receive more interactions regardless of true relevance. Popularity bias reinforces frequent exposure of popular items while under-recommending relevant, yet less popular ones. Directly learning from such data fails to capture true user preferences, leading to suboptimal recommendations. This research focuses on mitigating position bias and popularity bias in recommender systems. Specifically, I address position bias in learning-to-rank (LTR) systems and popularity bias in collaborative filtering (CF) models and social recommender systems based on graph neural networks. My work develops methods that overcome the limitations of existing approaches to mitigating position bias and popularity bias, enabling more relevant and personalized recommendations that align with users' preferences.
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