Microsoft Technology Licensing, LLC

Nikhil Dandekar

Peter Bailey

Michael Maxwell Cameron

Sanaz Ahari

Brian W MacDonald

Sarthak D Shah

Farid Hosseini

Xiaoxin Yin

Adam D Troy

Ryen William White

Xiao Lin Zhang

Junaid Ahmed

Tabreez Govani

Walter Sun

Sasi Parthasarathy

Adish Singla

Xin Chen

Yipeng Li

Jeff Huang

Dan Fain

Hugh Evan Williams

William Ramsey

Paul Nathan Bennett

Emre Mehmet Kiciman

Nicholas Eric Craswell

Nitin Agrawal

Scott Joseph Counts

Michael Ching

Chad Carson

Shubha Umesh Nabar

Ho John Lee

Nick Craswell

Aidan C Crook

Sebastian De La Chica

Hyun-Ju Seo

Paul Alexander Dow

Gheorghe Muresan

Amit Aggarwal

Daniel Robert Schwartz

Shuang Guo

Huizhong Duan

Evan Despault

Daniel C Fain

Aditya Pal

Search and browse trails are temporally-ordered sequences of web pages visited by a user during post-search query navigation beginning with a page associated with one of the search results. The trails can provide useful information for a number of search-related purposes. For example, these trails can be used to leverage the post-query behavior of other users to help the current user search more effectively and allow them to make more informed search interaction decisions. The trails can also be used to establish search results and refine search result rankings, select and evaluate deeplinks, and recommend multi-step trails as an alternative to or enhancement for existing search result presentation techniques.

Establishing search results and deeplinks using trails

Methods, systems, and computer-readable media are provided for updating a search index with new uniform resource locators (URLs) and spiking URLs with increased user interest. History data, provided from browser applications residing on users' computers that indicate URLs accessed by the users, is parsed to identify new/previously unknown URLs. The history data also indicates URLs in which there is increased interest based on a number of recent hits as compared to an average number of hits determined over time. Author postings of new URLs to social networking sites and a quality rating of the authors may also be used to identify and filter new URLs. Search indexes are updated with the new and spiking URL data. As such, lag time between posting of new URLs and spiking of URL interest and inclusion of this data in a search index is greatly decreased.

Updating a search index using reported browser history data

Computer-readable media and computerized methods are provided for identifying terms associated with one or more queries, including aspects directed to manipulating a list of terms associated with a query class by extracting terms, identifying equivalences or relevant terms, and condensing or expanding the list of terms. In embodiments, synonymous terms can be identified. An updated list of terms can be written to a storage location in association with a query or query class.

Identifying terms associated with queries

The author ranking technique described herein is a technique to rank authors in social media systems along various dimensions, using a variety of statistical methods for utilizing those dimensions. More particularly, the technique ranks authors in social media systems through a combination of statistical techniques that leverage usage metrics, and social and topical graph characteristics. In various exemplary embodiments, the technique can rank author authority by the following: 1) temporal analysis of link sharing in which authority is computed based on a user's propensity to provide early links to web pages that subsequently become popular; 2) topical authority based on the author's links and content updates in specific topic areas; and 3) popularity and influence based on nodal properties of authors.

Ranking authors in social media systems

Various technologies described herein pertain to using social activity data to personalize ranking of results returned by a computing operation for a user. For each of the results returned by the computing operation, a respective first affinity of the user to a corresponding result and a respective second affinity of the user to the corresponding result can be calculated and used for ranking the results. The respective first affinity of the user to the corresponding result can be calculated based on correlations between social activity data of the user and social activity data of a first group of historical users that clicked the corresponding result. Moreover, the respective second affinity of the user to the corresponding result can be calculated based on correlations between the social activity data of the user and social activity data of a second group of historical users that skipped the corresponding results.

Ranking based on social activity data

Methods are provided for re-ranking documents based on user-specific features. Search results are received from a non-contextual ranking system such that the search results are not specific toward a particular user, such as the user who submitted the search query. Contextual signals are received and provide user-specific features that are used to re-rank documents so that the most important and relevant documents are listed at the top of the list of search results. Each of the user-specific features are evaluated and compared to determine a new position of each document. A set of contextual search results is then generated based on the new positions.

Ranking contextual signals for search personalization

Computer-readable media and computerized methods for automatically organizing search results according to task groups are provided. The methods involve aggregating a gallery of entities (e.g., search queries that share a common categorization) into a query class and assigning a dictionary (e.g., list of terms that are drawn from various sources) to the query class. The task groups are identified from the list of terms within the dictionary. The process of identification includes analyzing patterns of user search behavior to select terms from the list of terms, which reflect popular user search intents, and ranking the selected terms based on predetermined parameters to produce an ordering. Based on the ordering, a set of the selected terms that are highest ranked are declared the task groups. The task groups are employed to arrange the search results on a UI display and to provide a consistent and intuitive format for refining a search.

Identifying task groups for organizing search results

User locality information can be used to improve various aspects of search results pages. Queries can be suggested based on the user location while excluding common query suggestions that involve an unrelated geographic entity. Deeplinks can also be modified to include location based suggestions. Additionally, results for specialized searches such as travel searches can be improved by employing user locality information.

Location aware search page elements

A computer system, method, and media for associating locations with ranked websites are provided. The computer system includes a search engine, a log database, and a location database that are employed to respond to search requests from users by returning appropriately ranked websites to the user. The websites are ranked using the location of the website and the location of the user to select websites to receive high ranks. Additionally, the search engine includes a correction feature that reevaluates locations for a website or user when a large number of obtained locations suggest a different location than a currently associated location for the website or the user.

Promoting websites based on location

Computer systems, methods, and media improving the relevance of the search results presented to a user for queries having an implicit local intent are provided. Utilizing the systems and methods described herein locally-biased queries are identified, despite such queries being void of any location-specific terms. A location of a computing device associated with the user is determined and localized web search results associated with the data item pair comprising the input query and the user's determined location are generated and presented to the user.

Relevance improvements for implicit local queries

Growing community of inventors

Bellevue, WA, United States of America

Nikhil Dandekar