用预训练语言模型增强推荐数据，解决稀疏与偏倚问题
SimAug: Enhancing Recommendation with Pretrained Language Models for Dense and Balanced Data Augmentation
- 基于PLM文本相似度生成数据增强
- 9个数据集上同时提升效果与公平性
- 轻量插件式设计，适配各类推荐系统
深度神经网络在协同过滤中广泛应用，依赖用户-物品交互数据学习嵌入表示。然而，交互数据常存在稀疏性和不平衡性：用户-物品交互记录有限导致性能不佳，热门物品互动过多则引入推荐偏差。为此，我们利用预训练语言模型（PLMs）融合文本信息，增强交互数据，生成更密集且均衡的数据集。具体提出一种简单有效的数据增强方法SimAug，基于PLM的文本相似度构建，可作为轻量级、即插即用组件集成至任意系统预处理阶段。在九个数据集上的实验表明，使用SimAug生成的增强数据训练后，推荐系统的效用和公平性均持续提升。代码已开源：https://github.com/YuyingZhao/SimAug。
原文摘要 · Abstract (English)
Deep Neural Networks (DNNs) are extensively used in collaborative filtering due to their impressive effectiveness. These systems depend on interaction data to learn user and item embeddings that are crucial for recommendations. However, the data often suffers from sparsity and imbalance issues: limited observations of user-item interactions can result in sub-optimal performance, and a predominance of interactions with popular items may introduce recommendation bias. To address these challenges, we employ Pretrained Language Models (PLMs) to enhance the interaction data with textual information, leading to a denser and more balanced dataset. Specifically, we propose a simple yet effective data augmentation method (SimAug) based on the textual similarity from PLMs, which can be seamlessly integrated to any systems as a lightweight, plug-and-play component in the pre-processing stage. Our experiments across nine datasets consistently demonstrate improvements in both utility and fairness when training with the augmented data generated by SimAug. The code is available at https://github.com/YuyingZhao/SimAug.
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