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    • 7. 发明授权
    • Click-through prediction for news queries
    • 新闻查询的点击式预测
    • US08719298B2
    • 2014-05-06
    • US12469692
    • 2009-05-21
    • Arnd Christian KonigMichael GamonQiang WuRoger P. MenezesMonwhea Jeng
    • Arnd Christian KonigMichael GamonQiang WuRoger P. MenezesMonwhea Jeng
    • G06F17/30
    • G06F17/30864
    • Described is estimating whether an online search query is a news-related query, and if so, outputting news-related results in association with other search results returned in response to the query. The query is processed into features, including by accessing corpora that corresponds to relatively current events, e.g., recently crawled from news and blog articles. A corpus of static reference data, such as an online encyclopedia, may be used to help determine whether the query is less likely to be about current events. Features include frequency-related data and context-related data corresponding to frequency and context information maintained in the corpora. Additional features may be obtained by processing text of the query itself, e.g., “query-only” features.
    • 描述了估计在线搜索查询是否是新闻相关查询,如果是,则输出与响应于该查询返回的其他搜索结果相关联的新闻相关结果。 该查询被处理成特征,包括通过访问对应于相对当前事件的语料库,例如最近从新闻和博客文章中爬行。 可以使用诸如在线百科全书的静态参考数据的语料库来帮助确定查询是否不太可能关于当前事件。 特征包括频率相关数据和对应于语料库中维护的频率和上下文信息的上下文相关数据。 可以通过处理查询本身的文本,例如“仅查询”特征来获得附加特征。
    • 9. 发明授权
    • Shopping search engines
    • 购物搜索引擎
    • US08700592B2
    • 2014-04-15
    • US12757095
    • 2010-04-09
    • Satya Pradeep KanduriMarcelo De BarrosMikhail ParakhinCynthia YuQiang Wu
    • Satya Pradeep KanduriMarcelo De BarrosMikhail ParakhinCynthia YuQiang Wu
    • G06F17/30
    • G06F17/30867
    • A web search system uses humans to rank the relevance of results returned for various sample search queries. The search results may be divided into groups allowing training and validation with the ranked results. Consistent guidelines for human evaluation allow consistent results across a number of people performing the ranking. After a machine learning categorization tool, such as MART, has been programmed and validated, it may be used to provide an absolute rank of relevance for documents returned, rather than a simple relative ranking, based, for example, on key word matches and click counts. Documents with lower relevance rankings may be excluded from consideration when developing related refinements, such as category and price sorting.
    • 网络搜索系统使用人类对各种样本搜索查询返回的结果的相关性进行排名。 搜索结果可以分为允许训练和验证与排名结果的组。 一致的人类评估指南可以让许多执行排名的人员获得一致的结果。 在机器学习分类工具(例如MART)已经被编程和验证之后,它可以用于提供返回的文档的绝对等级,而不是基于例如关键词匹配和点击的简单的相对排名 计数 具有较低相关性排名的文件可能在开发相关改进(例如类别和价格排序)时被排除在考虑之外。