围绕如何在浩瀚的中文网页中找到用户感兴趣的内容,提出了基于UCL(Uniform Content Locator)的“二阶过滤法”.它将媒体空间中的信息用UCL语义格(Semantic Cases based on UCL,SCU)表示,通过语义向量空间模型(Semantic Vector Space Mod...围绕如何在浩瀚的中文网页中找到用户感兴趣的内容,提出了基于UCL(Uniform Content Locator)的“二阶过滤法”.它将媒体空间中的信息用UCL语义格(Semantic Cases based on UCL,SCU)表示,通过语义向量空间模型(Semantic Vector Space Model,SVSM)对网页的语义矩阵进行分析计算,粗略筛选出用户感兴趣的网页;再借助精细语义逐句解读其内容,提取用户所关注的信息.根据用户的阅读行为动态了解用户的兴趣变化,建立用户兴趣的本体模型,并分析和定义了用户兴趣度的度量.实验验证了上述过滤方法的有效性,其测试结果同向量空间模型(Vector SpaceModel,VSM)进行了比较,性能明显优于VSM.展开更多
As the rapidly growing information on Web, information filtering techniques have been more and morewidely used. This paper introduces two main filtering methods ,named content-based and collaborative filtering; con-tr...As the rapidly growing information on Web, information filtering techniques have been more and morewidely used. This paper introduces two main filtering methods ,named content-based and collaborative filtering; con-trastively describes the key techniques in information filtering,including the representation,creation and rebuilding ofuser profile ,and similarity computing.展开更多
文摘围绕如何在浩瀚的中文网页中找到用户感兴趣的内容,提出了基于UCL(Uniform Content Locator)的“二阶过滤法”.它将媒体空间中的信息用UCL语义格(Semantic Cases based on UCL,SCU)表示,通过语义向量空间模型(Semantic Vector Space Model,SVSM)对网页的语义矩阵进行分析计算,粗略筛选出用户感兴趣的网页;再借助精细语义逐句解读其内容,提取用户所关注的信息.根据用户的阅读行为动态了解用户的兴趣变化,建立用户兴趣的本体模型,并分析和定义了用户兴趣度的度量.实验验证了上述过滤方法的有效性,其测试结果同向量空间模型(Vector SpaceModel,VSM)进行了比较,性能明显优于VSM.
文摘As the rapidly growing information on Web, information filtering techniques have been more and morewidely used. This paper introduces two main filtering methods ,named content-based and collaborative filtering; con-trastively describes the key techniques in information filtering,including the representation,creation and rebuilding ofuser profile ,and similarity computing.
