轻量对比文本嵌入提升生成推荐系统性能
HSTU-BLaIR: Lightweight Contrastive Text Embedding for Generative Recommender
- 用轻量对比模型BLaIR增强序列建模的推荐框架
- 在亚马逊和蒸汽平台数据集上表现优于大模型嵌入
- 适合资源有限但需高效语义理解的推荐场景
近年来，生成建模与预训练语言模型在推荐系统中展现出互补优势。本文提出HSTU-BLaIR，一种将层次化序列转换单元（HSTU）生成推荐器与轻量级对比文本嵌入模型BLaIR相结合的混合框架。该框架通过文本元数据引入语义信号，同时保留HSTU强大的序列建模能力。我们在两个电商数据集上评估：亚马逊评论2023数据集的三个子集和Steam数据集。与原始HSTU推荐器及采用OpenAI最先进的text-embedding-3-large模型嵌入的变体相比，尽管后者参数更多、训练语料更广，但本方法在几乎所有指标上表现更优。具体而言，HSTU-BLaIR在除一项指标外的所有指标上均超越该变体，且在另一项指标上持平。结果表明，对比文本嵌入在计算高效的推荐场景中具有显著有效性。
原文摘要 · Abstract (English)
Recent advances in recommender systems have underscored the complementary strengths of generative modeling and pretrained language models. We propose HSTU-BLaIR, a hybrid framework that augments the Hierarchical Sequential Transduction Unit (HSTU)-based generative recommender with BLaIR, a lightweight contrastive text embedding model. This integration enriches item representations with semantic signals from textual metadata while preserving HSTU's powerful sequence modeling capabilities. We evaluate HSTU-BLaIR on two e-commerce datasets: three subsets from the Amazon Reviews 2023 dataset and the Steam dataset. We compare its performance against both the original HSTU-based recommender and a variant augmented with embeddings from OpenAI's state-of-the-art \texttt{text-embedding-3-large} model. Despite the latter being trained on a substantially larger corpus with significantly more parameters, our lightweight BLaIR-enhanced approach -- pretrained on domain-specific data -- achieves better performance in nearly all cases. Specifically, HSTU-BLaIR outperforms the OpenAI embedding-based variant on all but one metric, where it is marginally lower, and matches it on another. These findings highlight the effectiveness of contrastive text embeddings in compute-efficient recommendation settings.
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