提出自适应归一化方法，显著缓解线性推荐模型的热门物品和局部关联偏差。
Why is Normalization Necessary for Linear Recommenders?
- 设计数据自适应归一化，动态调节用户与物品侧归一化强度
- 在6个数据集上提升长尾物品推荐效果达128.57%
- 可通用接入多种线性推荐模型，适合追求高效精准推荐的场景
尽管结构简单，线性自编码器（LAE）模型在推理速度上优于神经推荐模型且性能相当。但其面临两大挑战：流行度偏差（倾向于推荐热门物品）和邻域偏差（过度关注局部物品关联）。本文首次分析了两种现有归一化方法——随机游走与对称归一化对偏差的影响。理论研究表明，归一化方式显著影响物品的流行度与邻域偏差程度。受此启发，我们提出一种灵活的归一化方案——数据自适应归一化（DAN），通过调整用户与物品侧的归一化策略，适配不同数据集特性，有效控制两类偏差。DAN具有模型无关性，可无缝集成至多种LAE模型。实验表明，在六个基准数据集上，DAN增强的LAE模型持续提升性能，对长尾物品推荐效果最高提升128.57%，在无偏评估中提升达12.36%。
原文摘要 · Abstract (English)
Despite their simplicity, linear autoencoder (LAE)-based models have shown comparable or even better performance with faster inference speed than neural recommender models. However, LAEs face two critical challenges: (i) popularity bias, which tends to recommend popular items, and (ii) neighborhood bias, which overly focuses on capturing local item correlations. To address these issues, this paper first analyzes the effect of two existing normalization methods for LAEs, i.e., random-walk and symmetric normalization. Our theoretical analysis reveals that normalization highly affects the degree of popularity and neighborhood biases among items. Inspired by this analysis, we propose a versatile normalization solution, called Data-Adaptive Normalization (DAN), which flexibly controls the popularity and neighborhood biases by adjusting item- and user-side normalization to align with unique dataset characteristics. Owing to its model-agnostic property, DAN can be easily applied to various LAE-based models. Experimental results show that DAN-equipped LAEs consistently improve existing LAE-based models across six benchmark datasets, with significant gains of up to 128.57% and 12.36% for long-tail items and unbiased evaluations, respectively. Refer to our code in https://github.com/psm1206/DAN.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。