公开(公告)号：US08380722B2

公开(公告)日：2013-02-19

申请号：US12748903

申请日：2010-03-29

申请人： Zhicheng Dou ,  Junyan Chen ,  Ruihua Song ,  Ji-Rong Wen

发明人： Zhicheng Dou ,  Junyan Chen ,  Ruihua Song ,  Ji-Rong Wen

IPC分类号： G06F17/30

CPC分类号： G06F17/30887

摘要： This document describes tools for adjusting anchor text weight to provide more relevant search engine results. Specifically, these tools take advantage of a site-relationship model to consider relationships not only between an anchor text source site and a destination page but also relationships between multiple anchor text source sites to improve web searches. Consideration of these relationships aids in determining a new an anchor text weight, which in turn results in more relevant search results.

摘要翻译： 本文档描述了调整锚文本权重以提供更相关的搜索引擎结果的工具。 具体来说，这些工具利用站点关系模型来考虑不仅锚文本源站点和目标页面之间的关系，还考虑多个锚文本源站点之间的关系，以改进Web搜索。 考虑这些关系有助于确定新的锚文本权重，这又导致更相关的搜索结果。

公开(公告)号：US20110238644A1

公开(公告)日：2011-09-29

申请号：US12748903

申请日：2010-03-29

申请人： Zhicheng Dou ,  Junyan Chen ,  Ruihua Song ,  Ji-Rong Wen

发明人： Zhicheng Dou ,  Junyan Chen ,  Ruihua Song ,  Ji-Rong Wen

IPC分类号： G06F3/14 ,  G06F17/30

CPC分类号： G06F17/30887

摘要： This document describes tools for adjusting anchor text weight to provide more relevant search engine results. Specifically, these tools take advantage of a site-relationship model to consider relationships not only between an anchor text source site and a destination page but also relationships between multiple anchor text source sites to improve web searches. Consideration of these relationships aids in determining a new an anchor text weight, which in turn results in more relevant search results.

摘要翻译： 本文档描述了调整锚文本权重以提供更相关的搜索引擎结果的工具。 具体来说，这些工具利用站点关系模型来考虑不仅锚文本源站点和目标页面之间的关系，还考虑多个锚文本源站点之间的关系，以改进Web搜索。 考虑这些关系有助于确定新的锚文本权重，这又导致更相关的搜索结果。

公开(公告)号：US08977624B2

公开(公告)日：2015-03-10

申请号：US12871576

申请日：2010-08-30

申请人： Ruihua Song ,  Qi Yao ,  Junyan Chen

发明人： Ruihua Song ,  Qi Yao ,  Junyan Chen

IPC分类号： G06F17/30

CPC分类号： G06F17/30864 ,  G06F17/30887

摘要： Computer-readable media, computer systems, and computing devices facilitate enhancing a web index with uniform resource locator (URL)/non-encoding character (NEC) word pairs to facilitate relevance ranking of search results provided in response to a search query that includes NEC words. URLs are received from web pages and substrings extracted therefrom. Additional elements are received from the web page, word-broken into sequences of NEC words, and the NEC words are converted into encoding-language representations which are matched against the URL substrings to identify candidate URL/NEC pairs for utilization in relevance ranking.

摘要翻译： 计算机可读介质，计算机系统和计算设备有助于利用统一的资源定位器（URL）/非编码字符（NEC）字对来增强网络索引，以便于响应于包括NEC的搜索查询提供的搜索结果的相关性排名 话。 从从其中提取的网页和子串接收URL。 从网页接收附加元素，将其分词成NEC字的序列，并将NEC字转换成与URL子串相匹配的编码语言表示，以识别候选URL / NEC对以用于相关性排名。

公开(公告)号：US20120054192A1

公开(公告)日：2012-03-01

申请号：US12871576

申请日：2010-08-30

申请人： Ruihua Song ,  Qi Yao ,  Junyan Chen

发明人： Ruihua Song ,  Qi Yao ,  Junyan Chen

IPC分类号： G06F17/30

CPC分类号： G06F17/30864 ,  G06F17/30887

摘要： Computer-readable media, computer systems, and computing devices facilitate enhancing a web index with uniform resource locator (URL)/non-encoding character (NEC) word pairs to facilitate relevance ranking of search results provided in response to a search query that includes NEC words. URLs are received from web pages and substrings extracted therefrom. Additional elements are received from the web page, word-broken into sequences of NEC words, and the NEC words are converted into encoding-language representations which are matched against the URL substrings to identify candidate URL/NEC pairs for utilization in relevance ranking.

摘要翻译： 计算机可读介质，计算机系统和计算设备有助于利用统一的资源定位器（URL）/非编码字符（NEC）字对来增强网络索引，以便于响应于包括NEC的搜索查询提供的搜索结果的相关性排名 话。 从从其中提取的网页和子串接收URL。 从网页接收附加元素，将其分词成NEC字的序列，并将NEC字转换成与URL子串相匹配的编码语言表示，以识别候选URL / NEC对以用于相关性排名。

公开(公告)号：US20100076911A1

公开(公告)日：2010-03-25

申请号：US12238012

申请日：2008-09-25

申请人： Ning-Yi Xu ,  Junyan Chen ,  Rui Gao ,  Xiong-Fei Cai ,  Feng-Hsiung Hsu

发明人： Ning-Yi Xu ,  Junyan Chen ,  Rui Gao ,  Xiong-Fei Cai ,  Feng-Hsiung Hsu

IPC分类号： G06F15/18

CPC分类号： G06F17/30864 ,  G06N99/005

摘要： A method using a RankBoost-based algorithm to automatically select features for further ranking model training is provided. The method reiteratively applies a set of ranking candidates to a training data set comprising a plurality of ranking objects having a known pairwise ranking order. Each round of iteration applies a weight distribution of ranking object pairs, yields a ranking result by each ranking candidate, identifies a favored ranking candidate for the round based on the ranking results, and updates the weight distribution to be used in next iteration round by increasing weights of ranking object pairs that are poorly ranked by the favored ranking candidate. The method then infers a target feature set from the favored ranking candidates identified in the iterations.

摘要翻译： 提供了一种使用基于RankBoost的算法自动选择特征进行进一步排名模型训练的方法。 该方法重复地将一组排名候选应用于包括具有已知成对排序顺序的多个排名对象的训练数据集。 每轮迭代应用排序对象对的权重分布，由每个排名候选者产生排名结果，根据排名结果识别轮次的优选排名候选者，并通过增加下一次迭代更新要使用的权重分布 排名对象对的权重由受欢迎的排名候选人排名较差。 该方法然后从迭代中识别的优选排名候选推断目标特征集。

公开(公告)号：US08301638B2

公开(公告)日：2012-10-30

申请号：US12238012

申请日：2008-09-25

申请人： Ning-Yi Xu ,  Feng-Hsiung Hsu ,  Rui Gao ,  Xiong-Fei Cai ,  Junyan Chen

发明人： Ning-Yi Xu ,  Feng-Hsiung Hsu ,  Rui Gao ,  Xiong-Fei Cai ,  Junyan Chen

IPC分类号： G06F7/00 ,  G06F17/30 ,  G06F15/18

CPC分类号： G06F17/30864 ,  G06N99/005

摘要： A method using a RankBoost-based algorithm to automatically select features for further ranking model training is provided. The method reiteratively applies a set of ranking candidates to a training data set comprising a plurality of ranking objects having a known pairwise ranking order. Each round of iteration applies a weight distribution of ranking object pairs, yields a ranking result by each ranking candidate, identifies a favored ranking candidate for the round based on the ranking results, and updates the weight distribution to be used in next iteration round by increasing weights of ranking object pairs that are poorly ranked by the favored ranking candidate. The method then infers a target feature set from the favored ranking candidates identified in the iterations.

摘要翻译： 提供了一种使用基于RankBoost的算法自动选择特征进行进一步排名模型训练的方法。 该方法重复地将一组排名候选应用于包括具有已知成对排序顺序的多个排序对象的训练数据集。 每轮迭代应用排序对象对的权重分布，由每个排名候选者产生排名结果，根据排名结果识别轮次的优选排名候选者，并通过增加下一次迭代更新要使用的权重分布 排名对象对的权重由受欢迎的排名候选人排名较差。 该方法然后从迭代中识别的优选排名候选推断目标特征集。

公开(公告)号：US20090276414A1

公开(公告)日：2009-11-05

申请号：US12112826

申请日：2008-04-30

申请人： Jianfeng Gao ,  Qiang Wu ,  Jiangyun Song ,  Junyan Chen ,  Steven Yao

发明人： Jianfeng Gao ,  Qiang Wu ,  Jiangyun Song ,  Junyan Chen ,  Steven Yao

IPC分类号： G06F17/30

CPC分类号： G06F16/9535

摘要： Search results provided by a search engine (e.g., for the Internet) are improved and/or made more accurate by addressing the limited availability of human labeled training data for certain domains (e.g., languages other than English, within certain date ranges, corresponding to queries over a certain length, etc.). More particularly, a ranking model trained on in-domain data, for which a small amount of human labeled training data (e.g., query/URL pairs) is available (e.g., languages other than English) is adjusted based upon out-domain data, for which a large amount of human labeled training data (e.g., query/URL pairs) is available (e.g., English). Thus, even though the resulting adapted in-domain ranking model is used in the context of in-domain data (e.g., non-English) to provide search results, the search results are improved because they are influenced by an abundance of, albeit out-domain, human labeled training data.

摘要翻译： 搜索引擎提供的搜索结果（例如，对于互联网）进行改进和/或更准确地解决某些域名（例如，英语以外的语言，某些日期范围内的对应于 查询一定长度等）。 更具体地，针对域内数据进行训练的排名模型，基于域外数据来调整少量人类标记的训练数据（例如，查询/ URL对）可用（例如，除英语以外的语言） 为此，可以使用大量的人类标记的训练数据（例如，查询/ URL对）（例如，英语）。 因此，即使在域内数据（例如，非英语）的上下文中使用所产生的适应的域内排名模型来提供搜索结果，搜索结果被改进，因为它们受到丰富的影响，尽管 域名，人标签训练数据。