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Developing an adaptive search engine for e-commerce using a Web mining approach

机译:使用Web挖掘方法开发用于电子商务的自适应搜索引擎

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Discusses current work using an adaptive learning algorithm to dynamically create the content of an e-commerce search engine so that the implicit knowledge extracted by the Web text mining module can be provided in the B2B (business-to-business) portal. In this paper, we develop an algorithmic approach for automatically discovering implicit customer knowledge from the Internet by means of a Web mining method. Using a variation of the automatic thesaurus generation techniques, namely the self-organizing map (SOM) neural net, we have conducted several experiments in a specific domain in which we created a functional thesaurus of numerous supplier- and product-specific terms. Further, we applied such a thesaurus in a topic hierarchy-based text database, as an organized text source of a search engine for a novel B2B e-commerce portal.
机译:讨论使用自适应学习算法动态创建电子商务搜索引擎内容的当前工作,以便可以在B2B(企业对企业)门户中提供由Web文本挖掘模块提取的隐式知识。在本文中,我们开发了一种算法方法,该方法可通过Web挖掘方法从Internet自动发现隐式客户知识。使用自动同义词库生成技术的一种变体,即自组织映射(SOM)神经网络,我们在特定的领域中进行了几次实验,在其中创建了包含众多供应商和产品特定术语的功能词库。此外,我们在基于主题层次结构的文本数据库中应用了这样的词库,作为新型B2B电子商务门户的搜索引擎的有组织文本源。

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