Microsoft Technology Licensing, LLC

Kumaresh Pattabiraman

Anthony Duane Duerr

Kieran Richard McDonald

Christopher A Meyers

Tapas Kanungo

Kuansan Wang

Daniel Tunkelang

Ran Gilad-Bachrach

Nitin Agrawal

Parul Jain

Nipoon Malhotra

Santosh Kumar Kancha

Kevin Kao

Caleb Timothy Johnson

Keren Baruch

Kunal Mukesh Cholera

Luke Evans Simon

Jeffrey Warren Lee

Kevin Chuang

Ashok Ponnuswami

Nihit Desai

Mohammad Aleagha

Dezhen Li

Minhtu Nguyen

Jianbao Tao

Techniques for enhancing usability and electronic resource efficiency using job relevance are disclosed herein. In some embodiments, a list of a predetermined number of top job openings for a member of the social networking system is generated. The list is communicated to a device of the user for presentation in a user interface on the device. The user interface allows the member to browse through and provide an indication of a lack of relevancy of each of the list of the predetermined number of top job openings to the member and an indication of a reason for the lack of relevancy. Based on the user providing the indication of the lack of relevancy and the reason, a modified relevancy assessment of each of the plurality of job openings is generated. The list of the predetermined number of top job openings for the member is regenerated and communicated to the device.

Specialized user interfaces and processes for increasing user interactions with job postings in a social network/top jobs

In an example embodiment, a fast and space-efficient ranking mechanism is used for vertical searches. A precedence is assigned to a plurality of features based on the relative importance of those features to the provider of a vertical search engine. In an example embodiment, this relative importance may be explicitly indicated by the provider of the vertical search engine. A unique data structure may then be created that incorporates the precedence of each feature.

Precedence-based fast and space-efficient ranking

Techniques for assisting a user in conducting a search for online content are described. According to various embodiments, a set of search results is generated, in response to a user search query. A list of candidate facet-value pairs, for inclusion in a suggestion facet user interface element for display in-line with the set of search results, is then accessed. The suggestion facet user interface element may be configured, upon user selection, to filter the set of search results based on the corresponding facet-value pair. Thereafter, win rate information identifying win rates associated with each of the candidate facet-value pairs is accessed. A specific one of the candidate facet-value pairs having the highest win rate is then selected for inclusion in the suggestion facet user interface element.

Guided search

This patent application pertains to answer model comparison. One implementation can determine a first frequency at which an individual answer category appears in an individual slot on a query results page when utilizing a first model. The method can ascertain a second frequency at which the individual answer category appears in the individual slot on the query results page when utilizing a second model. The method can calibrate the second model so that the second frequency approaches the first frequency.

Answer model comparison

Methods and computer-storage media having computer-executable instructions embodied thereon that facilitate generating a machine-learned model for ranking search results using click-based data are provided. Data is referenced from user queries, which may include search results generated by general search engines and vertical search engines. A training set is generated from the search results and click-based judgments are associated with the search results in the training set. Based on click-based judgments, identifiable features are determined from the search results in a training set. Based on determining identifiable features in a training set, a rule set is generated for ranking subsequent search results.

Ranking search results using click-based data

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Sunnyvale, CA, United States of America

Kumaresh Pattabiraman