Microsoft Technology Licensing, LLC

Scott Kirk Imig

Robert L Rounthwaite

Ryen William White

Yang Song

Susan Theresa Dumais

Johnson T Apacible

Nikhil Kothari

Ye-Yi Wang

Li-wei He

Scott David Guthrie

Kenneth Ward Church

Silviu-Petru Cucerzan

Peter Bailey

Matthew R Richardson

Shanku Shivabrata Niyogi

Michael James Harder

Aravind Bala

Susan Chory

Hung-Chih Yang

Andres Sanabria

Robert Michael Howard

Matthew E Gibbs

Ting-Hao Yang

Emily Blythe Pitler

Gayathri Ravichandran Geetha

Nam Hoang Nguyen

Jean-Marie Larchevêque

Samuel D Paye, Jr

Functionality is described for identifying a discussion topic based on user interest information. The user interest information may originate from one or more sources, such as the user's search-related behavior. The functionality then provides an opportunity for the user to engage in a communication exchange associated with the identified discussion topic.

Identifying a discussion topic based on user interest information

The automatic search engine recommendation technique described herein automatically recommends topic-specific search engines for user queries. In one embodiment, it automatically matches each query submitted to a non-topic specific or general search engine with one or more vertical search engines using a recommendation model and a set of features. For a given query, one embodiment of the technique suggests vertical search engines and topic-specific search results along with the search results from the general search engine.

Automatic recommendation of vertical search engines

Architecture that automatically detects historical search contexts as well as behaviors related to a search query. Machine learning and hand-authored rules are employed to automatically identify search contexts. Historical information likely to be useful in the current context is surfaced. When a user enters a search query or executes another search behavior, past behaviors are exposed which are contextually related to the current behavior. The architecture also provides automatic discovery of historical contexts, features related to the contexts, and training or authoring of a system for classifying behavior into contexts, using some combination of the machine learning and/or hand-authored rules. A runtime system classifies the current user behavior into a context and surfaces contextual information to the user.

Auto-detection of historical search context

This patent application pertains to dynamic search interaction. One example includes an organizational component configured to obtain a search query from a user. The organizational component can also be configured to obtain related search queries. The organizational component can further be configured to organize the related search queries by topic and to estimate a relative likelihood that an intent of the user matches an individual topic. This example also includes an image generation component configured to cause the organized related search queries to be presented on a graphical user interface (GUI) in a manner that reflects the relative likelihood.

Dynamic search interaction

A searchable web site discovery and recommendation system and method for discovering and recommending searchable web sites to a user of a search engine. In general, given a search query by a user embodiments of the system and method discover and then recommend a list of searchable web sites. Embodiments of the system and method discover and recommend searchable web sites using client-side query behavior logs. A set of descriptive features is automatically extracted from the logs. In addition, embodiments of the system and method expand the initial search query by submitting the query to a general search engine and leveraging snippets from the search engine results. Embodiments of the system and method model a conditional probability of a searchable web site based on four components and the extracted descriptive features. A ranked list of recommended searchable web sites is obtained from the conditional probability and displayed to a user.

Searchable web site discovery and recommendation

Described is a search technology in which a search engine constructs a results page for a query that integrates suggested queries with the individual query results (e.g., displayed URLs). When rendered, the proximity of the suggested queries to their corresponding individual query result provides context as to the specific URL to which the suggested query is related. Suggested queries may appear alongside their associated search result, e.g., a displayed URL, and/or in an expandable panel proximate that individual search result. Suggested queries may appear within text accompanying a URL, and/or in a drop down menu following interaction with such text or the like. Related queries may be found by using a search result URL to find a query, by analyzing a search result's text snippet, by accessing historical data, and/or by accessing current user session data.

Contextual query suggestion in result pages

The described systems and methods are directed at defining web content navigation. The disclosed technique enables the development of web content for a scenario without having to create every navigational control that is needed to manage the flow through the web content in the scenario or to write non-declarative server-side logic to control navigation. This method determines a declaration in a source file for a web page. The declaration is associated with an object. The method determines the views associated with the object where each of the views includes a user-interface. A flow for navigating through the views is determined. The views are rendered in accordance with the navigational flow.

Systems and methods for defining web content navigation

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Redmond, WA, United States of America

Scott Kirk Imig