Conversational recommender systems(CRSs)focus on refining preferences and providing personalized recommendations through natural language interactions and dialogue history.Large language models(LLMs)have shown outstan...Conversational recommender systems(CRSs)focus on refining preferences and providing personalized recommendations through natural language interactions and dialogue history.Large language models(LLMs)have shown outstanding performance across various domains,thereby prompting researchers to investigate their applicability in recommendation systems.However,due to the lack of task-specific knowledge and an inefficient feature extraction process,LLMs still have suboptimal performance in recommendation tasks.Therefore,external knowledge sources,such as knowledge graphs(KGs)and knowledge bases(KBs),are often introduced to address the issue of data sparsity.Compared to KGs,KBs possess higher retrieval efficiency,making them more suitable for scenarios where LLMs serve as recommenders.To this end,we introduce a novel framework integrating LLMs with KBs for enhanced retrieval generation,namely LLMKB.LLMKB initially leverages structured knowledge to create mapping dictionaries,extracting entity-relation information from heterogeneous knowledge to construct KBs.Then,LLMKB achieves the embedding calibration between user information representations and documents in KBs through retrieval model fine-tuning.Finally,LLMKB employs retrievalaugmented generation to produce recommendations based on fused text inputs,followed by post-processing.Experiment results on two public CRS datasets demonstrate the effectiveness of our framework.Our code is publicly available at the link:https://anonymous.4open.science/r/LLMKB-6FD0.展开更多
目的研究核因子KB受体活化因子(receptor activator of NF-KB,RANK)及核因子KB受体活化因子配体(receptoractivator of NF-KB ligand,RANKL)和骨保护素(osteoprotegrin,OPG),在不同月龄雄性大鼠股骨表达的变化,探讨αD3对老龄大鼠RANK、...目的研究核因子KB受体活化因子(receptor activator of NF-KB,RANK)及核因子KB受体活化因子配体(receptoractivator of NF-KB ligand,RANKL)和骨保护素(osteoprotegrin,OPG),在不同月龄雄性大鼠股骨表达的变化,探讨αD3对老龄大鼠RANK、RANKL、OPG表达的影响。方法将6周龄、6月龄、24月龄、24月龄+αD3组Wistar大鼠分为甲、乙、丙、丁4组,每组15只。其中丁组为24月龄+αD3组,按0.05μg/kg/d-1灌胃,隔天1次,每周3次。连续10周。取左侧股骨远中干骺端1/2,采用RT-PCR测RANKL、RANK及OPG的mRNA的表达。取右侧股骨做免疫组化。结果与6周组相比,6月和24月组RANKLmRNA分别增加6.2倍和7.3倍,(P<0.05),OPGmRNA值随月龄逐步增加(P>0.05)。24月龄+αD3组较24月龄组RANK降低(P<0.05)、RANKL/OPG比值亦降低(P>0.05)。免疫组化结果显示RANKL、OPG表达于软骨和成骨细胞胞浆和胞核。24月龄+αD3干预组OPG表达较24月组增强。结论股骨RANKL/OPG值随月龄增加,有利于骨的吸收和转换;αD3有促进骨形成降低骨吸收的作用,其机制可能与降低RANK、RANKL/OPG比值有关。RANKL、OPG表达于同一类型的细胞,二者共同调节骨的代谢。展开更多
文摘Conversational recommender systems(CRSs)focus on refining preferences and providing personalized recommendations through natural language interactions and dialogue history.Large language models(LLMs)have shown outstanding performance across various domains,thereby prompting researchers to investigate their applicability in recommendation systems.However,due to the lack of task-specific knowledge and an inefficient feature extraction process,LLMs still have suboptimal performance in recommendation tasks.Therefore,external knowledge sources,such as knowledge graphs(KGs)and knowledge bases(KBs),are often introduced to address the issue of data sparsity.Compared to KGs,KBs possess higher retrieval efficiency,making them more suitable for scenarios where LLMs serve as recommenders.To this end,we introduce a novel framework integrating LLMs with KBs for enhanced retrieval generation,namely LLMKB.LLMKB initially leverages structured knowledge to create mapping dictionaries,extracting entity-relation information from heterogeneous knowledge to construct KBs.Then,LLMKB achieves the embedding calibration between user information representations and documents in KBs through retrieval model fine-tuning.Finally,LLMKB employs retrievalaugmented generation to produce recommendations based on fused text inputs,followed by post-processing.Experiment results on two public CRS datasets demonstrate the effectiveness of our framework.Our code is publicly available at the link:https://anonymous.4open.science/r/LLMKB-6FD0.
文摘目的研究核因子KB受体活化因子(receptor activator of NF-KB,RANK)及核因子KB受体活化因子配体(receptoractivator of NF-KB ligand,RANKL)和骨保护素(osteoprotegrin,OPG),在不同月龄雄性大鼠股骨表达的变化,探讨αD3对老龄大鼠RANK、RANKL、OPG表达的影响。方法将6周龄、6月龄、24月龄、24月龄+αD3组Wistar大鼠分为甲、乙、丙、丁4组,每组15只。其中丁组为24月龄+αD3组,按0.05μg/kg/d-1灌胃,隔天1次,每周3次。连续10周。取左侧股骨远中干骺端1/2,采用RT-PCR测RANKL、RANK及OPG的mRNA的表达。取右侧股骨做免疫组化。结果与6周组相比,6月和24月组RANKLmRNA分别增加6.2倍和7.3倍,(P<0.05),OPGmRNA值随月龄逐步增加(P>0.05)。24月龄+αD3组较24月龄组RANK降低(P<0.05)、RANKL/OPG比值亦降低(P>0.05)。免疫组化结果显示RANKL、OPG表达于软骨和成骨细胞胞浆和胞核。24月龄+αD3干预组OPG表达较24月组增强。结论股骨RANKL/OPG值随月龄增加,有利于骨的吸收和转换;αD3有促进骨形成降低骨吸收的作用,其机制可能与降低RANK、RANKL/OPG比值有关。RANKL、OPG表达于同一类型的细胞,二者共同调节骨的代谢。
