HSTU-BLaIR: Lightweight Contrastive Text Embedding for Generative Recommender

• URL: http://arxiv.org/abs/2504.10545v3
• Date: Thu, 19 Jun 2025 07:04:38 GMT
• Title: HSTU-BLaIR: Lightweight Contrastive Text Embedding for Generative Recommender
• Authors: Yijun Liu,
• Abstract summary: We propose HSTU-BLaIR, a hybrid framework that augments the generative recommender with a lightweight contrastive text embedding model.<n>We evaluate HSTU-BLaIR on two e-commerce datasets: three subsets from the Amazon Reviews 2023 dataset and the Steam dataset.
• Score: 8.466223794246261
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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