AI 中文总结
本研究针对隐式反馈推荐系统的位置偏差与流行度偏差，开发了适配排序学习、协同过滤及图神经网络社交推荐系统的无偏方法，以生成更贴合用户偏好的个性化推荐。
AI 中文摘要
推荐系统通常依赖隐式反馈（如点击）来推断用户偏好，但此类数据天生易受多种偏差影响，包括位置偏差和流行度偏差。位置偏差指排名更高的项目无论真实相关性如何都会获得更多交互；流行度偏差则会强化热门项目的频繁曝光，同时低估推荐相关但不太热门的项目。直接从这类数据中学习无法捕捉用户真实偏好，会导致次优推荐。本研究聚焦于缓解推荐系统中的位置偏差与流行度偏差，具体而言，针对排序学习（LTR）系统中的位置偏差、协同过滤（CF）模型及基于图神经网络的社交推荐系统中的流行度偏差进行处理，所开发的方法克服了现有偏差缓解方法的局限，能够生成更贴合用户偏好的相关且个性化推荐。
英文摘要
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.
Journal refIn 20th ACM Conference on Recommender Systems (RecSys '26), September 27-October 02, 2026, Minneapolis, MN, USA. ACM, New York, NY,USA, 7 pages
