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Knowledgebasedsystemsmakerecommendationsbyknowledgereasoningandneednotlearnusers’pref...
Knowledge based systems make recommendations by knowledge reasoning and need not learn users’ preferences first. There are three kinds of knowledge in knowledge based recommender systems, namely user knowledge, product knowledge and function knowledge. To give users broad choices over the ever rich information on the world wide web, grid technology is incorporated into the knowledge based recommender systems to enable the distributed electronic commerce recommendation. The hierarchical model of the knowledge grid is explored in this paper. Knowledge grid is exploited to provide a normal resource space for the constituents in the recommender systems and a uniform platform of knowledge sharing and service for recommender systems to conduct knowledge search, knowledge discovery, knowledge fusion, knowledge based workflow scheduling and execution. To realize a semantic interaction environment the semantic grid is exploited for semantic publication, discovery, integration, scheduling, allocation, execution and management of the grid resources. Ontology technology is incorporated into the knowledge grid model to annotate the heterogeneous grid resources and endow them with semantics.
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知识系统提出建议是以知识推理和需要不学习用户的偏爱作为前提。这里有三种知识在知识基地推荐人系统,即用户知识,产品知识,功能知识。给用户广泛的精选甚至超过了曾经在万维网丰富的情报,网格技术被包括到知识基地推荐人系统来启用分发电子商务建议。的层次模型知识网格被探索在这篇论文。知识网格被利用提供一个标准资源空间为组分在推荐人系统,知识共享的一个均平台,服务为推荐人系统行为知识搜索,知识发现,知识融合,知识基地工作流时间表和执行。意识到一个语义相互作用环境语义网格被利用对语义刊物,发现,积分,时间表,分配,执行和管理的网格资。本体论技术被包括到知识网格模型注释异质格资,赋予他们同语义学。
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