一种用于可控序列推荐的红undancy减少方法

中文摘要 AI 辅助

序列推荐必须在长尾物品分布和流行度驱动的集中性下运行，这常常迫使从业者在短列表准确性和长尾曝光之间进行权衡。在这项工作中，我们研究特征去相关作为一种机制，用于塑造点积序列推荐器中的表示几何，并分析这如何反过来影响流行度驱动的集中性。我们提出了一种去相关正则化训练框架，该框架通过一个辅助的冗余减少项来增强下一项预测，并用BT-SR实例化，其使用Barlow Twins目标。为了在没有合成扰动的情况下形成标签一致的正样本对，我们将共享相同下一项目标的用户历史配对。除了准确性之外，我们提供了一种几何分析，展示去相关如何抑制用户表示空间中共享的低秩方向，这些方向可能给流行物品带来全局评分优势，并且我们引入了一种基于桶的对齐集中度指标来量化这种效应。在五个公开基准上的实验表明，BT-SR持续提高下一项排序质量，而去相关强度作为一个简单的控制旋钮，在头部和尾部物品之间重新分配准确性，实现准确性-曝光权衡。我们的分析还揭示了头部与尾部曝光的影响在不同数据集间存在差异，反映了去相关与数据时间结构之间的相互作用。

英文摘要

Sequential recommendation must operate under long-tailed item distributions and popularity-driven concentration, often forcing practitioners to trade short-list accuracy against long-tail exposure. In this work, we study feature decorrelation as a mechanism for shaping representation geometry in dot-product sequential recommenders, and analyze how this, in turn, affects popularity-driven concentration. We propose a decorrelation-regularized training framework that augments next-item prediction with an auxiliary redundancy-reduction term, and instantiate it with BT-SR, which uses the Barlow Twins objective. To form label-consistent positive pairs without synthetic corruptions, we pair user histories that share the same next-item target. Beyond accuracy, we provide a geometric analysis showing how decorrelation suppresses shared low-rank directions in the user representation space that can give popular items a global scoring advantage, and we introduce a bucket-based alignment concentration metric to quantify this effect. Experiments on five public benchmarks show that BT-SR consistently improves next-item ranking quality, while the decorrelation strength acts as a simple control knob that reallocates accuracy across head and tail items, enabling accuracy-exposure trade-offs. Our analysis also reveals that the impact on head-vs-tail exposure differs across datasets, reflecting interactions between decorrelation and data temporal structure.

发表机构

• Yandex
• Applied AI Institute（应用人工智能研究所）
• AXXX
• Lomonosov MSU（莫斯科国立大学）
• HSE University（高等经济大学）

机构由 AI 辅助整理，请以论文原文为准。