Microsoft Technology Licensing, LLC

Stephen Robertson

Dmitriy Meyerzon

Michael James Taylor

Vladimir Tankovich

Hugo Zaragoza

Michael B Taylor

Stephen Walker

Justin Zobel

Stefan Isbein Larimore

John Guiver

Mihai Petriuc

Thomas Minka

Techniques to perform relative ranking for search results are described. An apparatus may include an enhanced search component operative to receive a search query and provide ranked search results responsive to the search query. The enhanced search component may comprise a resource search module operative to search for resources using multiple search terms from the search query, and output a set of resources having some or all of the search terms. The enhanced search component may also comprise a proximity generation module communicatively coupled to the resource search module, the proximity generation module operative to receive the set of resources, retrieve search term position information for each resource, and generate a proximity feature value based on the search term position information. The enhanced search component may further comprise a resource ranking module communicatively coupled to the resource search module and the proximity generation module, the resource ranking module to receive the proximity feature values, and rank the resources based in part on the proximity feature values. Other embodiments are described and claimed.

Techniques to perform relative ranking for search results

Data structures, stored on various types of computer-readable media, include information related to user profiles and/or to various documents. The information included in these data structures is arranged and stored in manner that allows for rapid user profile updating to be performed as new or changed documents are processed in a document filtering system.

Fast adaptive document filtering

Methods to enable optimization of discontinuous rank metrics are described. The search scores associated with a number of search objects are written as score distributions and these are converted into rank distributions for each object in an iterative process. Each object is selected in turn and the score distribution of the selected object is compared to the score distributions of each other object in turn to generate a probability that the selected object is ranked in a particular position. For example, with three documents the rank distribution may give a 20% probability that a document is ranked first, a 60% probability that the document is ranked second and a 20% probability that the document is ranked third. In some embodiments, the rank distributions may then be used in the optimization of discontinuous rank metrics.

Optimization of discontinuous rank metrics

Search results of a search query on a network are ranked according to a scoring function that incorporates anchor text as a term. The scoring function is adjusted so that a target document of anchor text reflect the use of terms in the anchor text in the target document's ranking. Initially, the properties associated with the anchor text are collected during a crawl of the network. A separate index is generated that includes an inverted list of the documents and the terms in the anchor text. The index is then consulted in response to a query to calculate a document's score. The score is then used to rank the documents and produce the query results.

System and method for incorporating anchor text into ranking search results

A field-weighted search combines statistical information for each term across document fields in a suitably weighted fashion. Both field-specific term frequencies and field and document lengths are considered to obtain a field-weighted document weight for each query term. Each field-weighted document weight can then be combined in order to generate a field-weighted document score that is responsive to the overall query.

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Stephen Robertson