公开(公告)号：US08065311B2

公开(公告)日：2011-11-22

申请号：US12147417

申请日：2008-06-26

Applicant: Mingyu Wang ,  Weibin Zhu ,  Ying Li ,  Qiaolin Mao

Inventor： Mingyu Wang ,  Weibin Zhu ,  Ying Li ,  Qiaolin Mao

IPC: G06F7/00 ,  G06F17/30

CPC classification number: G06F17/30864 ,  G06Q30/02

Abstract: Described is a paid search advertising technology in which advertisements associated with bidding keywords are ranked by relevance when returning one or more advertisements in a response to a query. A relevance score is computed for an advertisement based on the bidding keyword and page data (text and/or other page content) of the advertisement. The relevance score may be based on a similarity vector score computed from a keyword vector and page data vector relationship, combined with a proximity score computed from the keyword's bigram set and the page data bigram set. When a query is received, advertisements are selected based on the proximity of the query to each advertisement's bidding keyword, providing candidate scores. Each candidate score is modified (e.g., multiplied) into a final score based on its respective advertisement's relevance score. The final scores are then used to re-rank the advertisements relative to one another.

Abstract translation: 描述了一种付费搜索广告技术，其中在与查询的响应中返回一个或多个广告时，与投标关键词相关联的广告被排列为相关性。 基于广告的出价关键字和页面数据（文本和/或其他页面内容）来计算广告的相关性分数。 相关性分数可以基于从关键字向量和页面数据向量关系计算的相似性向量分数，以及从关键字的二值组合和页面数据二进制集合计算的邻近分数。 当接收到查询时，基于查询对每个广告的出价关键词的接近度来选择广告，提供候选分数。 基于其各自的广告的相关性得分，将每个候选得分修改（例如，乘以）到最终得分中。 然后，最终得分用于相对于彼此重新排列广告。

公开(公告)号：US20090327265A1

公开(公告)日：2009-12-31

申请号：US12147417

申请日：2008-06-26

Applicant: Mingyu Wang ,  Weibin Zhu ,  Ying Li ,  Qiaolin Mao

Inventor： Mingyu Wang ,  Weibin Zhu ,  Ying Li ,  Qiaolin Mao

IPC: G06F17/30

CPC classification number: G06F17/30864 ,  G06Q30/02

Abstract: Described is a paid search advertising technology in which advertisements associated with bidding keywords are ranked by relevance when returning one or more advertisements in a response to a query. A relevance score is computed for an advertisement based on the bidding keyword and page data (text and/or other page content) of the advertisement. The relevance score may be based on a similarity vector score computed from a keyword vector and page data vector relationship, combined with a proximity score computed from the keyword's bigram set and the page data bigram set. When a query is received, advertisements are selected based on the proximity of the query to each advertisement's bidding keyword, providing candidate scores. Each candidate score is modified (e.g., multiplied) into a final score based on its respective advertisement's relevance score. The final scores are then used to re-rank the advertisements relative to one another.

Abstract translation: 描述了一种付费搜索广告技术，其中在与查询的响应中返回一个或多个广告时，与投标关键词相关联的广告被排列为相关性。 基于广告的出价关键字和页面数据（文本和/或其他页面内容）来计算广告的相关性分数。 相关性分数可以基于从关键字向量和页面数据向量关系计算的相似性向量分数，以及从关键字的二值组合和页面数据二进制集合计算的邻近分数。 当接收到查询时，基于查询对每个广告的出价关键词的接近度来选择广告，提供候选分数。 基于其各自的广告的相关性得分，将每个候选得分修改（例如，乘以）到最终得分中。 然后，最终得分用于相对于彼此重新排列广告。