Microsoft Technology Licensing, LLC

Janine Crumb

Brian C Beckman

Ming Zhou

Jianfeng Gao

Vijay Mital

Darryl Ellis Rubin

Olivier Colle

Eric Benjamin Watson

Gregory Nicholas Hullender

Stelios Paparizos

Gaurav Sareen

Mu Li

Girish Kumar

An Yan

Rangan Majumder

Chris L Anderson

Krishna Chaitanya Gade

Jason A Wolf

Bhuvan Middha

Ajay Nair

Bama Ramarathnam

Haoyong Zhang

Silviu Petru Cucerzan

Vishnu Challam

Gennadii M Tertychnyi

James Gemmell

Elliott K Olds

Yajuan Lv

Jason Douglas

Ranking domains for search engines is provided herein. To rank a domain, contributing domains associated with the domain are identified. Additionally, the maturity of each of the contributing domains is determined. A rank for the domain is then determined based at least in part on the maturity of each of the contributing domains. The domain rankings may then be used to order results for search queries.

Ranking domains using domain maturity

Interactive and dynamic exploring of filter criteria for large amounts of numerical data having multiple dimensions is described. Exploration occurs without excessive amounts of computation or querying of a database based on a data set summary that may be obtained from a server in a search system and applied by a client device. A data set is summarized so as to capture relationships between distributions of items in the data set along multiple dimensions. To explore filter criteria, a user may interactively adjust a range of values that act as a filter criteria along a dimension and determine an effect on a distribution of items along another dimension. The client device may apply the data summary in reverse to determine, based on user input specifying aspects of a distribution along one dimension, filter criteria for another dimension that would result in the specified distribution.

Assisted filtering of multi-dimensional data

Methods and computer-readable media are provided for determining suggested queries. A user enters a search website, and the user is identified based on a user identification. Suggested queries are determined based on a group associated with the user. This association is created by extracting queries from data logs, categorizing the queries into groups based on their respective subject matter, associating the user with one or more groups, and determining suggested queries for each group. The suggested queries are communicated for display.

Providing query suggestions

Candidate suggestions for correcting misspelled query terms input into a search application are automatically generated. A score for each candidate suggestion can be generated using a first decoding pass and paths through the suggestions can be ranked in a second decoding pass. Candidate suggestions can be generated based on typographical errors, phonetic mistakes and/or compounding mistakes. Furthermore, a ranking model can be developed to rank candidate suggestions to be presented to a user.

Query speller

Computerized methods and systems for generating a suggested query list with suggested search terms displayed as highlighted text utilizing a user-defined query are provided. Query search terms are received by a user-interface display. Upon inputting query search terms, the user-interface automatically generates a suggested query list. The suggested query list is associated with the query search term and the suggested query list is comprised of at least one suggested search term. A query suggestion architecture determines if the query search term and the suggested search term are a match, and if so, highlights the suggested search term that is not a match. The user interface displays the highlighted terms to assist in refining a search. The present invention further provides a stemming algorithm that extracts the root form of the query search term.

Support for reverse and stemmed hit-highlighting

Evaluating an electronic document in connection with a search. An external source provides data for use in evaluating an electronic document retrieved by a search engine. A first confidence level of the electronic document is determined based on the externally provided data. The first confidence level indicates a likelihood that the electronic document is undesirable. A second confidence level of the electronic document is determined based on attributes of the electronic document. The second confidence level indicates a likelihood that the electronic document is unsatisfactory with respect to a search. A rating for the electronic document generated as a function of the determined first confidence level and the determined second confidence level is used to categorize the electronic document as unsatisfactory in connection with a received search request.

Search engine spam detection using external data

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Seattle, WA, United States of America

Janine Crumb