Microsoft Technology Licensing, LLC

Microsoft Corporation

Dou Shen

Zheng Chen

Ying Li

Benyu Zhang

Hua-Jun Zeng

Jian Wang

Wei-Ying Ma

Jian-Tao Sun

Yi Zhang

Weizhu Chen

Xuanhui Wang

Andrew S Crane

Qiang Yang

Heng Zhang

Valeri Liborski

Philip Lee

Sahil Thaker

In this invention, systems and methods for providing keywords for advertising are provided. After a user searches for another user in a social network, the webpage or blog of the queried user is retrieved, and keywords are extracted from this webpage. The keywords may be extracted from the user's profile on the social network (e.g., favorite sports, music artists, etc.), or keywords may be extracted from the text of the webpage (e.g., comments that comprise the blog entries). Once extracted, these keywords may then be used by an advertising system to provide targeted advertisements to the user.

Ad retrieval for user search on social network sites

A system, media, and method for selecting future queries are provided. The selected future queries are used to transmit appropriate online advertising to a user that issues queries to a search engine. The search engine is coupled to a prediction component that predicts what subject the user is going to be interested in and when the user will be interested in the subject. The prediction component returns a future query using statistical language models representing a query history of the user and aggregate query histories for a community of users.

Predicting future queries from log data

Systems, methods, and computer storage media having computer-executable instructions embodied thereon that facilitate advertisement selection using advertising units. An entity that is a sequence of two or more words is referenced. The entity includes substrings comprising a portion of the entity. Search data in association with the entity is compared to corresponding search data in association with the substrings of the entity. Based on the comparison, it is determined that the entity comprises an advertisable unit that functions as a unit for purposes of selecting an advertisement for display. The advertisable unit is used to select an advertisement to be presented to the user.

Facilitating advertisement selection using advertisable units

The claimed subject matter is directed to constructing query hierarchies in response to a query request. To construct a query hierarchy, a list of related candidate queries is generated in response to the received query request. The list of related candidate queries is generated by determining the relative coverage of information shared by the candidate queries and the query request. Relationships between the submitted query request and the candidate queries in the list are determined based upon the extent of relative coverage of information shared by the candidate queries and the query request. A query hierarchy is then constructed to reflect the determined relationships between the query request and the candidate queries.

Constructing web query hierarchies from click-through data

Described is a technology by which an intermediate taxonomy is processed (e.g., offline) with respect to a target taxonomy to determine relationship values between categories represented in the intermediate taxonomy and the target taxonomy. The relationship values are used to construct a bridging classifier for use in online query processing to relate queries to categories in the target taxonomy. The relation is based on each target category's relationship to one or more categories that were represented in the intermediate taxonomy. Further, only a relevant subset of the categories represented in the intermediate taxonomy may be chosen for use in the bridging classifier, e.g., based on relative probability scores and/or mutual information scores computed between the categories represented in the intermediate taxonomy and categories in the target taxonomy.

Building bridges for web query classification

A method and system for calculating the significance of a sentence within a document is provided. The summarization system calculates the significance of the sentences of a document and selects the most significant sentences as the summary of the document. The summarization system calculates the significance of a sentence based on the 'important' words of the document that are contained within the sentence. The summarization system calculates the importance of words of the document using various scoring techniques and then combines the scores to classify a word as important or not important. The summarization system can then be used to identify significant sentences of the document based on the important words that a sentence contains and select significant sentences as a summary of the document.

Method and system for summarizing a document

Methods and systems are provided for performing a comparative search. In one example, the comparative search is performed over a network, such as the web, or a database. In one exemplary implementation, a user transmits a plurality of queries which represent the topics that a user wants to compare, and a computing system can automatically retrieve and rank web pages or documents based on both their relevance to queries and the comparative contents they contain. In one such example, the comparative pages are displayed in a pair or other form of a grouping. In another example, comparative results having similar contents may be clustered into meaningful themes.

Comparative web search

A method and system for classifying display pages based on automatically generated summaries of display pages. A web page classification system uses a web page summarization system to generate summaries of web pages. The summary of a web page may include the sentences of the web page that are most closely related to the primary topic of the web page. The summarization system may combine the benefits of multiple summarization techniques to identify the sentences of a web page that represent the primary topic of the web page. Once the summary is generated, the classification system may apply conventional classification techniques to the summary to classify the web page. The classification system may use conventional classification techniques such as a Na&#xef;ve Bayesian classifier or a support vector machine to identify the classifications of a web page based on the summary generated by the summarization system.

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Redmond, WA, United States of America

Dou Shen