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公开(公告)号：US20110010307A1
公开(公告)日：2011-01-13
申请号：US12698087
申请日：2010-02-01
申请人： Keith M. Bates , Julian Paas , Jiang Su , Biao Wang , Bo Xu , Pendar Yousefi
发明人： Keith M. Bates , Julian Paas , Jiang Su , Biao Wang , Bo Xu , Pendar Yousefi
CPC分类号： G06Q30/02 , G06Q30/0282 , G06Q30/0631
摘要： In a data processing system, a method of recommending articles and products to a user is disclosed. The method creates a frequency vector in relation to the content of an article, frequency vectors in relation each of one or more products from intermediate data. The method compares the vectors to determine a content similarity measure, and provides as output a list of one or more products having the highest content similarity measures. The method may also determine a correlation measure. An electronic data processing system for recommending articles and products to a user is also disclosed. The system includes modules to receive article information and product information, a correlation module to determine a content similarity measure between the article and each of the products and, a multiplexer module for providing a list comprising the article and the products associated having the highest content similarity measure.
摘要翻译： 在数据处理系统中，公开了向用户推荐文章和产品的方法。 该方法相对于物品的内容产生一个频率矢量，其中关于来自中间数据的一个或多个产品中的每一个的频率矢量。 该方法比较向量以确定内容相似性度量，并且作为输出提供具有最高内容相似性度量的一个或多个产品的列表。 该方法还可以确定相关度量。 还公开了一种用于向用户推荐物品和产品的电子数据处理系统。 该系统包括用于接收物品信息和产品信息的模块，用于确定物品和每个产品之间的内容相似性度量的相关模块，以及用于提供包括物品的列表和相关联的具有最高内容相似性的产品的多路复用器模块 测量。
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公开(公告)号：US20090300547A1
公开(公告)日：2009-12-03
申请号：US12360516
申请日：2009-01-27
申请人： Keith M. Bates , Julian Paas , Biao Wang , Bo Xu , Pendar Yousefi
发明人： Keith M. Bates , Julian Paas , Biao Wang , Bo Xu , Pendar Yousefi
CPC分类号： G06F16/954
摘要： A system and method for recommending on-line articles and documents to users is disclosed. The method provides an article widget user interface and a full-screen widget user interfaces to allow a user to rate articles, to preview articles, to filter articles based on category, article length, or other characteristics. A recommender system is configured to provide a continually refreshing list of recommended articles to the user via the user interfaces. The system comprises a module configured to monitor the user's explicit and implicit interactions with the user interfaces, and provides a refreshed list of recommended articles accordingly. The recommender system may be configured to use a package of approaches including rule-based, content-based or collaborative filtering approaches including Slope, Co-Visitation, Mwinnow and Clustering/Co-clustering.
摘要翻译： 公开了向用户推荐在线物品和文件的系统和方法。 该方法提供文章小部件用户界面和全屏小部件用户界面，以允许用户对文章进行评分，预览文章，基于类别，文章长度或其他特征来过滤文章。 推荐系统被配置为通过用户界面向用户提供不断更新的推荐文章列表。 系统包括被配置为监视用户与用户界面的显式和隐含交互的模块，并且相应地提供所刷新的推荐文章列表。 推荐系统可以被配置为使用包括基于规则的，基于内容的或协作过滤方法的一系列方法，包括Slope，Co-Visitation，Mwinnow和Clustering / Co-clustering。
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公开(公告)号：US20100185568A1
公开(公告)日：2010-07-22
申请号：US12355945
申请日：2009-01-19
申请人： Keith M. Bates , Jiang Su , Bo Xu , Biao Wang
发明人： Keith M. Bates , Jiang Su , Bo Xu , Biao Wang
CPC分类号： G06N20/00
摘要： A system and method to classify web-based documents as articles or non-articles is disclosed. The method generates a machine learning model from a human labelled training set which contains articles and non-articles. The machine learning model is applied to new articles to label them as articles or non-articles. The method generates the machine learning model based on content, such as text and tags of the web-based documents. The invention also provides for devices which incorporate the machine learning model, allowing such devices to classify documents as articles or non-articles.
摘要翻译： 公开了将基于网络的文档分类为文章或非文章的系统和方法。 该方法从包含文章和非文章的人类标记训练集中生成机器学习模型。 机器学习模型适用于新的文章，将其标注为文章或非文章。 该方法基于内容（如基于Web的文档的文本和标签）生成机器学习模型。 本发明还提供了结合机器学习模型的设备，允许这样的设备将文档分类为文章或非文章。
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