基于全局列表式学习与渐进双加权的推荐系统流行度偏差缓解方法
Mitigating Popularity Bias in Recommendation with Global Listwise Learning and Progressive Bi-Weighting
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中文总结 AI 辅助
针对推荐系统中IPS类去偏方法受局部无偏目标和倾向估计不准限制的问题，提出融合多项似然与IPS、带平滑机制的双加权及渐进策略的Mult-BiW框架，理论支撑充分且实验效果优于SOTA基线。
中文摘要 AI 辅助
在推荐系统中，用户反馈通常服从长尾分布，这导致许多推荐算法因过度偏向热门物品而加剧流行度偏差。为缓解该问题，近期研究采用逆倾向评分（Inverse Propensity Scoring, IPS），通过对用户-物品交互重新加权来平衡训练数据。然而，基于IPS的方法的效果往往受限于局部无偏目标与不准确的倾向估计。\n本文提出双加权多项似然（Multinomial Likelihood with Bi-Weighting, Mult-BiW）方法以解决上述局限。首先，我们提出名为Mult-IPS的去偏差框架，将多项似然与IPS相结合，捕捉用户在整个物品集合上的全局无偏偏好。其次，我们开发了双加权（Bi-Weighting, BiW）策略，该策略同时利用倾向评分与收集模型，并引入平滑机制以提升倾向估计的鲁棒性。我们进一步提供了理论分析，确立了经验偏差的上界，并刻画了收集模型的最优形式。第三，为缓解过度加权对表示学习的负面影响，我们设计了渐进双加权策略，可逐步从判别式表示学习过渡到流行度去偏差。在真实世界数据集上的大量实验表明，Mult-BiW的性能始终优于当前最优基线方法。
英文摘要
In recommender systems, user feedback typically follows a long-tail distribution, which leads many recommendation algorithms to exacerbate popularity bias by disproportionately favoring popular items. To mitigate this issue, recent studies have employed Inverse Propensity Scoring (IPS) to rebalance training data via reweighting user-item interactions. However, the effectiveness of IPS-based approaches is often constrained by locally unbiased objectives and inaccurate propensity estimation. In this paper, we propose Multinomial Likelihood with Bi-Weighting (Mult-BiW) to address these limitations. First, we introduce a debiasing framework, termed Mult-IPS, which integrates multinomial likelihood with IPS to capture global and unbiased user preferences over the entire item set. Second, we develop a Bi-Weighting (BiW) strategy that jointly leverages propensity scores and a collection model, incorporating a smoothing mechanism to enhance the robustness of propensity estimation. We further provide theoretical analyses that establish an upper bound on the empirical bias and characterize the optimal form of the collection model. Third, to mitigate the adverse effects of aggressive reweighting on representation learning, we design a Progressive Bi-Weighting strategy that gradually transitions from discriminative representation learning to popularity debiasing. Extensive experiments on real-world datasets show that Mult-BiW consistently outperforms state-of-the-art baselines.
发表机构
- University of Montreal（蒙特利尔大学）
- School of Economics and Management, Beihang University（北京航空航天大学经济管理学院）
- College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机科学技术学院）
- School of Management, Fudan University（复旦大学管理学院）
- School of Economics and Management, Tsinghua University（清华大学经济管理学院）
- Department of Computer Science and Operations Research, University of Montreal（蒙特利尔大学计算机科学与运筹学系）
机构由 AI 辅助整理，请以论文原文为准。
