用自编码器融合侧信息，解决冷启动推荐难题
Shallow AutoEncoding Recommender with Cold Start Handling via Side Features
- 基于自编码器架构，融合用户和物品的侧信息
- 冷启动场景下准确率显著优于传统协同过滤方法
- 适合新用户/新物品场景，且不引入额外偏差
用户和物品的冷启动在推荐系统工业应用中构成重大挑战。通过补充用户-物品交互数据中的元数据是常见解决方案，但常伴随引入额外偏差。本文提出一种增强型EASE模型，无缝整合用户与物品侧信息以应对冷启动问题。该基于自编码器的简洁方法提供闭式解，在冷门物品场景下利用丰富内容信号，同时在数据稀疏环境下优化用户表示。重要的是，该方法在有效推荐冷启动物品的同时处理冷启动用户，且不引入额外偏差，并在暖启动场景中保持强性能。实验表明，相比以往协同过滤方法，本模型在推荐准确性和鲁棒性上均有提升。此外，该模型可作为未来对比研究的强有力基线。
原文摘要 · Abstract (English)
User and item cold starts present significant challenges in industrial applications of recommendation systems. Supplementing user-item interaction data with metadata is a common solution-but often at the cost of introducing additional biases. In this work, we introduce an augmented EASE model that seamlessly integrates both user and item side information to address these cold start issues. Our straightforward, autoencoder-based method produces a closed-form solution that leverages rich content signals for cold items while refining user representations in data-sparse environments. Importantly, our method strikes a balance by effectively recommending cold start items and handling cold start users without incurring extra bias, and it maintains strong performance in warm settings. Experimental results demonstrate improved recommendation accuracy and robustness compared to previous collaborative filtering approaches. Moreover, our model serves as a strong baseline for future comparative studies.
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