用语义搜索增强生成式推荐，提升冷启动与长序列用户效果
GLoSS: Generative Language Models with Semantic Search for Sequential Recommendation
- 结合大模型与稠密检索，通过语义匹配替代传统词法匹配
- 在三个亚马逊数据集上召回率提升超30%，优于主流基线模型
- 适合处理冷启动和长历史用户，尤其在玩具与运动品类表现突出
我们提出生成式低秩语言模型与语义搜索（GLoSS），一种融合大语言模型与稠密检索的序列推荐框架。与依赖词法匹配的GPT4Rec不同，GLoSS采用语义搜索获取超越词汇匹配的相关物品。查询生成采用4比特量化微调的LlaMA-3模型，结合低秩适配（LoRA），实现在普通硬件上的高效训练与推理。我们在Beauty、Toys、Sports三个真实世界亚马逊评论数据集上评估，结果表明其达到当前最优性能：相比传统基于ID的基线，召回率@5分别提升33.3%、52.8%、15.2%，NDCG@5分别提升30.0%、42.6%、16.1%。同时，相比其他基于LLM的推荐器如P5、GPT4Rec、LlamaRec和E4SRec，GLoSS在召回率@5上分别取得4.3%、22.8%、29.5%的提升。用户分段分析显示，该模型在Amazon Toys和Sports数据集中对冷启动用户表现优异，在Beauty数据集中受益于更长的用户历史，展现出对不同交互长度的鲁棒性。
原文摘要 · Abstract (English)
We propose Generative Low-rank language model with Semantic Search (GLoSS), a generative recommendation framework that combines large language models with dense retrieval for sequential recommendation. Unlike prior methods such as GPT4Rec, which rely on lexical matching via BM25, GLoSS uses semantic search to retrieve relevant items beyond lexical matching. For query generation, we employ 4-bit quantized LlaMA-3 models fine-tuned with low-rank adaptation (LoRA), enabling efficient training and inference on modest hardware. We evaluate GLoSS on three real-world Amazon review datasets: Beauty, Toys, and Sports, and find that it achieves state-of-the-art performance. Compared to traditional ID-based baselines, GLoSS improves Recall@5 by 33.3%, 52.8%, and 15.2%, and NDCG@5 by 30.0%, 42.6%, and 16.1%, respectively. It also outperforms LLM-based recommenders such as P5, GPT4Rec, LlamaRec and E4SRec with Recall@5 gains of 4.3%, 22.8%, and 29.5%. Additionally, user segment evaluations show that GLoSS performs particularly well for cold-start users in the Amazon Toys and Sports datasets, and benefits from longer user histories in Amazon Beauty dataset, demonstrating robustness across different levels of interaction lengths.
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