研究人员开发了一个名为软Token融合的新框架，用于将数值和嵌入特征集成到大型语言模型（LLMs）的推荐系统中。该方法解决了当前基于LLM的推荐系统主要处理离散文本Token的局限性，使其能够更好地利用连续和密集特征。在亚马逊推荐基准上的实验表明，与现有的LLM基线相比，该方法提高了检索性能，并且交互式融合模块比简单的拼接更有效。 AI
影响 通过实现对不同数据类型的更好集成，增强了LLM在推荐系统中的能力。
排序理由 该集群包含一篇详细介绍LLM在推荐系统中新方法的学术论文。
在 arXiv cs.IR (Information Retrieval) 阅读 →
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arXiv:2607.10016v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as backbone architectures for recommender systems because of their strong sequence modeling and representation learning capabilities. However, most LLM-based recommenders operate …
Large language models (LLMs) are increasingly used as backbone architectures for recommender systems because of their strong sequence modeling and representation learning capabilities. However, most LLM-based recommenders operate primarily on discrete textual tokens, whereas prac…
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