当AI代理多到找不着，这家“工厂”专治选择困难，精准检索领域专属智能体。
arXiv:2608.09934v1 Announce Type: new Abstract: Large language model (LLM) agents improve task performance by decomposing problems into role-specializ…
电商属性分类新方法：人机协作迭代自举，无需大量标注即可构建高质量搜索属性体系。
arXiv:2606.04909v1 Announce Type: cross Abstract: E-commerce platforms in emerging markets often operate with underdeveloped product catalogs that con…
被SIGIR 2026录用的研究，提出双视角理解LLM的新范式，为个性化推荐注入大模型推理能力。
arXiv:2605.26717v1 Announce Type: cross Abstract: Adapting large language models (LLMs) for personalized recommendation requires aligning their genera…
提出BEAR方法，让大模型在推荐中主动适应beam search解码，提升推荐准确性。
arXiv:2601.22925v3 Announce Type: replace-cross Abstract: Recent years have seen a rapid surge in research leveraging Large Language Models (LLMs) for…
用检索增强大模型读取直播间的“似曾相识”线索，跨会话证据让风险预判更聪明
arXiv:2601.16027v2 Announce Type: replace Abstract: The rise of live streaming has transformed online interaction, enabling massive real-time engageme…
LLM检索让广告推荐更稳定可预测，看论文如何用大模型优化推荐系统
arXiv:2605.21969v1 Announce Type: cross Abstract: Traditional ads recommendation systems have primarily focused on optimizing for prediction accuracy …
生物医学实体链接新突破：利用生成式重排序提升实体匹配精度，已被SIGIR 2026接收。
arXiv:2605.22501v1 Announce Type: new Abstract: Despite recent progress, Biomedical Entity Linking (BEL) with large language models (LLMs) remains com…