发现大模型用未加权数据反而更好，挑战推荐系统传统做法。
Does Weighting Improve Matrix Factorization for Recommender Systems?
- 系统测试多种加权策略与矩阵分解算法
- 大模型在无加权数据下表现不输甚至优于加权训练
- 揭示低容量模型和特定正则化下加权的收益
矩阵分解是推荐系统中用于Top-N推荐和协同过滤的常用方法。在隐式反馈数据（如点击）上，常见做法是提高观测交互的权重。这一策略已被证明对某些算法有效。本文系统研究了多种加权方案与矩阵分解算法。令人意外的是，我们发现使用未加权数据训练的表现可与加权数据相当，甚至在某些情况下更优，尤其在大模型上。这一发现挑战了传统认知。然而，我们仍识别出加权在低容量模型及特定正则化方案下具有优势。此外，我们推导出高效算法，可精确最小化此前被认为计算不可行的若干加权目标。本工作为推荐系统中加权、正则化与模型容量之间的相互作用提供了全面分析。
原文摘要 · Abstract (English)
Matrix factorization is a widely used approach for top-N recommendation and collaborative filtering. When implemented on implicit feedback data (such as clicks), a common heuristic is to upweight the observed interactions. This strategy has been shown to improve performance for certain algorithms. In this paper, we conduct a systematic study of various weighting schemes and matrix factorization algorithms. Somewhat surprisingly, we find that training with unweighted data can perform comparably to, and sometimes outperform, training with weighted data, especially for large models. This observation challenges the conventional wisdom. Nevertheless, we identify cases where weighting can be beneficial, particularly for models with lower capacity and specific regularization schemes. We also derive efficient algorithms for exactly minimizing several weighted objectives that were previously considered computationally intractable. Our work provides a comprehensive analysis of the interplay between weighting, regularization, and model capacity in matrix factorization for recommender systems.
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