Microsoft Technology Licensing, LLC

Filip Andrzej Radlinski

Nick Craswell

Pushmeet Kohli

Peter Bailey

Yoram Bachrach

Eric J Horvitz

Susan Theresa Dumais

Paul Nathan Bennett

Andrew Donald Gordon

John Michael Winn

Sanaz Ahari

Nitin Agrawal

Thore Graepel

Milad Shokouhi

Matteo Venanzi

Ulrich Paquet

Lucas Bordeaux

Daniel T Schwartz

Martin Szummer

Aidan C Crook

Timothy C Hoad

Katja Hofmann

Muhammad Aatif Awan

Song Zhou

Emine Yilmaz

Michael Stanislaw Kosinski

Li Quan Khoo

Bodo Billerbeck

Likhita Krishnamurthy

Brian Brost

In various examples there is an information retrieval system which has a memory storing an index of items, each item having one or more features of a plurality of features. The index includes, for each item, an indication of the features of that item; and is configured at least according to the features. The memory stores a plurality of questions about the features of the items. The information retrieval system has a question selector that selects at least one of the questions on the basis of at least an expected information gain of the question with respect to the items. A search component searches the index using answers to the selected questions received from a user.

Information retrieval system

Checking and/or completing for data grids is described such as for grids having rows and columns of cells at least some of which contain data values such as numbers or categories. In various embodiments predictive probability distributions are obtained from an inference engine for one or more of the cells and the predictive probability distributions are used for various tasks such as to suggest values to complete blank cells, highlight cells having outlying values, identify potential errors, suggest corrections to potential errors, identify similarities between cells, identify differences between cells, cluster rows of the data grid, and other tasks. In various embodiments a graphical user interface displays a data grid and provides facilities for completing, error checking/correcting, and analyzing data in the data grid.

Checking and/or completion for data grids

Personality aggregation and web browsing is described, for example, to find personality profiles of website audiences for use in recommendation systems, advertising systems, or other web services. In an embodiment natural browsing sequences of users who have given their consent are submitted to a pattern matching process to identify personality trait scores serendipitously occurring in the sequences. In an embodiment, an aggregator combines the personality trait scores by website to obtain audience personality profiles. In an example, a machine learning process carries out the aggregation and enables audience personality profiles of other websites to be predicted. For example, a random decision forest is trained using the natural browsing sequences having identified personality trait scores and once trained, is used to predict personality trait scores of other websites.

Personality aggregation and web browsing

A method of controlling access to content such as web sites on the intranet or interne is described. For example, the method comprises receiving an address of content to be accessed and obtaining similarity of the address to previously labeled addresses of other content items. The similarity is based on co-occurrence of addresses of content items in records of browsing sessions from many consenting users. For example, a browsing session record comprises addresses of content items accessed by a user in a time period during which the user is actively accessing content. A co-occurrence of addresses of content items is the existence of the addresses in the same browsing session record. Access to the content is then controlled on the basis of the similarity.

Website access control

A system and method are provided for detecting entity information contained within search results. The detected entity information can be used to determine a category of entity as well as a specific entity within the search results. Entity information can be extracted from the documents associated with the search results. This information can be used as part of the information for an entity card, which can be displayed to a user in conjunction with and/or in place of the search results.

Entity detection and extraction for entity cards

Measuring duplication in search results is described. In one example, duplication between a pair of results provided by an information retrieval system in response to a query is measured. History data for the information retrieval system is accessed and query data retrieved, which describes the number of times that users have previously selected either or both of the pair of results, and a relative presentation sequence of the pair of results when displayed at each selection. From the query data, a fraction of user selections is determined in which a predefined combination of one or both of the pair of results were selected for a predefined presentation sequence. From the fraction, a measure of duplication between the pair of results is found. In further examples, the information retrieval system uses the measure of duplication to determine an overall redundancy value for a result set, and controls the result display accordingly.

Measuring duplication in search results

Methods and computer-readable media are provided for interleaving multiple sets of blended search results to identify a preferred set of blended search results for a search query or for a category of search queries. A search query is received and routed to multiple ranking systems, which return search results that are blended into sets of blended search results. Interleaving experiments are performed to determined, based on user preferences, which set of blended search results are preferred for that particular search query. Based on the outcome, a preferred blending algorithm is identified.

Optimizing blending algorithms using interleaving

Inferring query intent in information retrieval is described. In an example reformulations of an initial query by a user are used to create a query neighborhood. In the example, the query neighborhood is used to identify a set of possibly related queries. First and higher order reformulations of the initial query may be used to expand the query neighborhood. In an example precision can be improved by reducing the query neighborhood to more closely related queries for example, two queries can be connected if they are often clicked for the same document. In an example two queries can be connected using a random walk and all pairs of queries that are not connected by a random walk of less than a fixed threshold are removed. The connected queries can be used to form clusters and weights can be applied in order to determine the most likely related queries.

Query intent in information retrieval

Methods and computer-readable media are provided for improving the efficiency of the interleaving process. Once it is determined that a search query is to be used in an interleaving experiment, a primary ranking system and a secondary ranking system are analyzed to determine whether their respective search results for that search query would be identical. If the search results would be identical, the search query is routed only to the primary ranking system and not the secondary ranking system. If, however, it is determined that the search results would not be identical, both the primary ranking system and the secondary ranking system receive the search query for processing.

Efficient use of computational resources for interleaving

The claimed subject matter provides systems and/or methods that facilitate diversifying search results to improve searching and/or personalized searching. An interface component can receive a query. Additionally, a result diversification component can modify diversity of a set of search results obtained by a search engine in response to a query based at least in part upon one or more dimensions of diversity. The dimensions of diversity can include document topic, document type, document genre, domain of document, document age, location associated with the document, commercial characteristics of the document, user intent, and the like.

Diversifying search results for improved search and personalization

Growing community of inventors

London, United Kingdom

Filip Andrzej Radlinski