用文本语义构建图模型，实现零样本推荐新高度
Leveraging Language Semantics for Collaborative Filtering with TextGCN and TextGCN-MLP: Zero-Shot vs In-Domain Performance
- 直接在标题嵌入上做无参数图卷积，融合语义与图传播
- 零样本下超越现有方法，跨域推荐性能显著提升
- 适合关注跨域推荐与模型泛化能力的研究者
近年来，众多方法尝试利用大语言模型（LLM）将物品文本信息融入推荐系统。现有方法主要聚焦于微调LLM生成推荐或整合基于LLM的嵌入至下游模型。本文采用后一方向，提出TextGCN，直接在基于LLM的物品标题嵌入上应用无参数图卷积层，而非传统方法中的基于ID的嵌入学习。通过结合语言语义与图消息传递，该架构在零样本场景下达到当前最优性能，显著优于先前方法。此外，我们引入TextGCN-MLP，通过可训练的多层感知机并使用对比损失进行优化，在推荐基准上实现领域内性能最优。然而，TextGCN-MLP的零样本表现仍低于TextGCN，凸显了领域专精与零样本泛化之间的权衡。代码已开源。
原文摘要 · Abstract (English)
In recent years, various approaches have been proposed to leverage large language models (LLMs) for incorporating textual information about items into recommender systems. Existing methods primarily focus on either fine-tuning LLMs to generate recommendations or integrating LLM-based embeddings into downstream models. In this work, we follow the latter direction and propose \textbf{TextGCN}, which applies parameter-free graph convolution layers directly over LLM-based item-title embeddings, instead of learning ID-based embeddings as in traditional methods. By combining language semantics with graph message passing, this architecture achieves state-of-the-art zero-shot performance, significantly outperforming prior approaches. Furthermore, we introduce \textbf{TextGCN-MLP}, which extends TextGCN with a trainable multilayer perceptron trained using a contrastive loss, achieving state-of-the-art in-domain performance on recommendation benchmarks. However, the zero-shot performance of TextGCN-MLP remains lower than that of TextGCN, highlighting the trade-off between in-domain specialization and zero-shot generalization. We release our code on github at \href{https://github.com/ChernovAndrey/TFCE}{github.com/ChernovAndrey/TFCE}.
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。