提出几何协同过滤算法，提升推荐系统泛化能力
Geometric Collaborative Filtering with Convergence
- 基于物品元数据的几何结构设计新损失函数
- 在Movielens20M和Netflix上优于现有方法
- 适合关注推荐系统理论与性能优化的研究者
隐变量协同过滤是建模用户点击行为的标准方法，但其数学性质研究有限，尤其在防止对身份过拟合方面。本文引入协同过滤中的泛化差距概念，分析隐变量模型的泛化性能。提出一种几何上界，导出新的损失函数，并利用物品元数据的几何结构提升推荐效果。通过最小化该损失，设计出名为GeoCF的新协同过滤算法。实验表明，GeoCF在Movielens20M、Netflix及两个大规模内部数据集上均优于现有方法。本工作为理解协同过滤的泛化性提供了理论基础。
原文摘要 · Abstract (English)
Latent variable collaborative filtering methods have been a standard approach to modelling user-click interactions due to their simplicity and effectiveness. However, there is limited work on analyzing the mathematical properties of these methods in particular on preventing the overfitting towards the identity, and such methods typically utilize loss functions that overlook the geometry between items. In this work, we introduce a notion of generalization gap in collaborative filtering and analyze this with respect to latent collaborative filtering models. We present a geometric upper bound that gives rise to loss functions, and a way to meaningfully utilize the geometry of item-metadata to improve recommendations. We show how these losses can be minimized and gives the recipe to a new latent collaborative filtering algorithm, which we refer to as GeoCF, due to the geometric nature of our results. We then show experimentally that our proposed GeoCF algorithm can outperform other all existing methods on the Movielens20M and Netflix datasets, as well as two large-scale internal datasets. In summary, our work proposes a theoretically sound method which paves a way to better understand generalization of collaborative filtering at large.
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