The integration of Large Language Models(LLMs)into e-commerce platforms has significantly enhanced user experience through personalized recommendations and automated customer support.However,existing Retrieval-Augment...The integration of Large Language Models(LLMs)into e-commerce platforms has significantly enhanced user experience through personalized recommendations and automated customer support.However,existing Retrieval-Augmented Generation(RAG)frameworks face challenges when applied to e-commerce product Question Answering(QA),such as handling extensive product catalogs,ensuring timely knowledge updates,and maintaining efficient retrieval performance.In this paper,we propose ItemRAG,a novel framework that combines RAG with item-based knowledge computing to address these challenges.ItemRAG decouples QA templates from specific products by leveraging a dynamic knowledge graph,enabling efficient updates and reducing the size of the knowledge base.The framework includes state analysis to capture user intent and context,grouped indexing for efficient retrieval,and knowledge computing to dynamically generate accurate answers.Experimental results demonstrate that decoupled-based ItemRAG significantly outperforms the Coupled-based RAG approaches(CoupledRAG)in retrieval accuracy and generation quality,achieving higher precision,recall,F1-score,and factual correctness.Our work highlights the efficacy of integrating the knowledge graph with RAG to enhance LLM-based e-commerce customer service systems.展开更多
COMPUTATIONAL knowledge vision[1]is emphasized as a novel perspective or field in this paper.It first proposes the visual hierarchy and its connection to knowledge,stating that knowledge is a justified true belief.To ...COMPUTATIONAL knowledge vision[1]is emphasized as a novel perspective or field in this paper.It first proposes the visual hierarchy and its connection to knowledge,stating that knowledge is a justified true belief.To further the previous research,we concisely summarize our recent works and suggest a new direction that knowledge is also a thought framework in vision.展开更多
A growing interest in producing and sharing computable biomedical knowledge artifacts(CBKs) is increasing the demand for repositories that validate, catalog, and provide shared access to CBKs. However, there is a lack...A growing interest in producing and sharing computable biomedical knowledge artifacts(CBKs) is increasing the demand for repositories that validate, catalog, and provide shared access to CBKs. However, there is a lack of evidence on how best to manage and sustain CBK repositories. In this paper, we present the results of interviews with several pioneering CBK repository owners. These interviews were informed by the Trusted Repositories Audit and Certification(TRAC) framework. Insights gained from these interviews suggest that the organizations operating CBK repositories are somewhat new, that their initial approaches to repository governance are informal, and that achieving economic sustainability for their CBK repositories is a major challenge. To enable a learning health system to make better use of its data intelligence, future approaches to CBK repository management will require enhanced governance and closer adherence to best practice frameworks to meet the needs of myriad biomedical science and health communities. More effort is needed to find sustainable funding models for accessible CBK artifact collections.展开更多
基金supported by the National Natural Science Foundation of China(Nos.62506166,U2441285,and 62222605)the Natural Science Foundation of Jiangsu Province(No.SBK20250401456)+1 种基金the China Postdoctoral Science Foundation(No.2025M774283)the DiDi GAIA Collaborative Research Funds(No.CCF-DiDi GAIA202507).
文摘The integration of Large Language Models(LLMs)into e-commerce platforms has significantly enhanced user experience through personalized recommendations and automated customer support.However,existing Retrieval-Augmented Generation(RAG)frameworks face challenges when applied to e-commerce product Question Answering(QA),such as handling extensive product catalogs,ensuring timely knowledge updates,and maintaining efficient retrieval performance.In this paper,we propose ItemRAG,a novel framework that combines RAG with item-based knowledge computing to address these challenges.ItemRAG decouples QA templates from specific products by leveraging a dynamic knowledge graph,enabling efficient updates and reducing the size of the knowledge base.The framework includes state analysis to capture user intent and context,grouped indexing for efficient retrieval,and knowledge computing to dynamically generate accurate answers.Experimental results demonstrate that decoupled-based ItemRAG significantly outperforms the Coupled-based RAG approaches(CoupledRAG)in retrieval accuracy and generation quality,achieving higher precision,recall,F1-score,and factual correctness.Our work highlights the efficacy of integrating the knowledge graph with RAG to enhance LLM-based e-commerce customer service systems.
基金supported in part by the Natural Science Foundation of China(62303361)in part by the Hainan Provincial Natural Science Foundation of China(623QN266)+2 种基金the Fundamental Research Funds for the Central Universities(WUT:233110002)in part by the University-Industry Collaborative Education Program(231002531131826)in part by the National Key R&D Program of China(2018AAA0101502)
文摘COMPUTATIONAL knowledge vision[1]is emphasized as a novel perspective or field in this paper.It first proposes the visual hierarchy and its connection to knowledge,stating that knowledge is a justified true belief.To further the previous research,we concisely summarize our recent works and suggest a new direction that knowledge is also a thought framework in vision.
文摘A growing interest in producing and sharing computable biomedical knowledge artifacts(CBKs) is increasing the demand for repositories that validate, catalog, and provide shared access to CBKs. However, there is a lack of evidence on how best to manage and sustain CBK repositories. In this paper, we present the results of interviews with several pioneering CBK repository owners. These interviews were informed by the Trusted Repositories Audit and Certification(TRAC) framework. Insights gained from these interviews suggest that the organizations operating CBK repositories are somewhat new, that their initial approaches to repository governance are informal, and that achieving economic sustainability for their CBK repositories is a major challenge. To enable a learning health system to make better use of its data intelligence, future approaches to CBK repository management will require enhanced governance and closer adherence to best practice frameworks to meet the needs of myriad biomedical science and health communities. More effort is needed to find sustainable funding models for accessible CBK artifact collections.
