Yahoo! Inc.

Excalibur Ip, LLC

Deepa Joshi

Shyam Kapur

Jignashu Parikh

Mike Wexler

Ashish Kumar Shukla

John Thrall

Ingrid Lestiyo

Winton Henry Davies

Vik Singh

Michael Mathieson

Kalpana Ravinarayanan

Ethan Kan

Michael Smith

Michael J Wilkens

A method and system for generating a topic page for a search query on a search webpage includes receiving a query at the search webpage on a client. The query is transmitted from the search webpage on the client to a search engine on a server. A topic page generator available to the search engine analyzes the query to identify a plurality of dimensions. One or more content modules that match one or more of the dimensions are selected from a plurality of sources based on a weight associated with each of the content modules. The weight defines the ranking of a content module. The content modules for the plurality of dimensions are glued together and presented on the topic page in the order of the corresponding weight of the content modules. The order of presentation identifies the relevancy of the content modules to the query. The presented topic page provides the most relevant content modules for the query, and for a user located in a specific geo location.

Algorithmically generated topic pages

A method and apparatus for optimizing content on a topic page includes receiving a query for a topic at a topic page on a client and transmitting the query from the topic page on the client to a web application on a server. The web application includes algorithm to analyze the query to identify a plurality of content modules that match the query. The content modules are identified from anyone of a branded source or an un-branded source. One or more module performance indicators are computed for each of the identified content modules. An aggregate module performance indicator for each of the plurality of content modules is generated from the one or more computed module performance indicators. One or more content modules from the identified plurality of content modules are automatically selected for rendering on the topic page based on the aggregate module performance indicator associated with each of the identified content modules. The resulting topic page includes content modules from just the branded source, just the un-branded source or an aggregate of content modules from both branded source and un-branded source providing optimal content that is most relevant to the query.

Algorithmically choosing when to use branded content versus aggregated content

Methods for guiding user moderation at a topic page by confidence levels includes presenting a topic page in response to a query. The topic page includes a plurality of modules with content that match the query. The topic page is associated with a confidence level and with one or more page attributes that define the characteristics of the topic page and the modules included therein. One or more modifications to the topic page are received as part of customization of the topic page. The modifications include a plurality of page attributes that define the modification and a plurality of user attributes of a user performing the modification. The modifications are evaluated based on the page attributes and user attributes including confidence levels associated with the topic page and the user. The modifications are implemented based on the evaluation. The implemented modifications enhance the quality and confidence level of the topic page.

Guiding user moderation by confidence levels

Methods and systems for reconstructing a full query based on a partial query are disclosed. Existing interfaces for search engines may be rigid and require users to submit full queries to perform searched. The methods and systems described herein may solve these problems by allowing a flexible way for users to submit a partial query and reconstruct a full query based on the partial query. A search may then be performed using the reconstructed query.

Method and apparatus for reconstructing a search query

A system is described for discovering query intent based on search queries and concept networks. The system may construct frequency vectors from log data corresponding to a submitted query and at least one related query submitted to one or more search engines. The system may also construct a query intent vector based on the frequency vectors. The query intent vector may include frequency scores that represent the intent of the query.

Discovering query intent from search queries and concept networks

Methods for optimizing webpage content by micro-bucket testing user customization to the webpage include presenting a plurality of modules at a webpage based on a request from a user. The modules define an intent of the webpage. A change defining customization to one or more modules within the webpage is detected. A test case representing the change is automatically generated. The generated test case is a modified webpage having the customization. The webpage is presented to a first segment of users as a control page and the modified webpage is presented to a second segment of users in response to a request for the webpage. User interaction by the first and segment of users is monitored at the webpage and the modified webpage to determine website metrics of the corresponding webpages. The website metrics is used in defining a new control page of the webpage from the modified webpage or retaining the webpage as the control page.

Micro-bucket testing for page optimization

A search query that includes a first spelling of a term is received. A second spelling of the term is generated. A first set of items that are associated with the first spelling of the term is accessed, and a second set of items that are associated with the second spelling of the term is accessed. A comparison is performed between the first set to the second set. A determination is made as to how to respond to the search query based, at least in part, on the comparison. The response may include a suggested search query that includes the second spelling of the term. The second spelling may be a suggested spelling correction or an alternative spelling. The response may include search results that are relevant to a search query that includes the first spelling of the term, the second spelling, or both the first and second spelling.

Discovering alternative spellings through co-occurrence

Multiple concept networks are generated from subsets of received queries. These concept networks can be used in various ways to enhance response to subsequent queries. In one embodiment, concept networks can be merged into a larger concept network that can be used to infer a user's likely intent given a query. In another embodiment, suggestions for related searches obtained using different concept networks can be merged or aggregated. Other users for concept networks in query processing, including assisted search, are also described.

Systems and methods for managing and using multiple concept networks for assisted search processing

A system and method for selecting a proxy keyword for an unknown document. An unknown document is received by a receiver. A plurality of candidate documents and corresponding keywords are determined for the unknown document. Using the keywords from the candidate documents, proxy keywords are determined for the unknown document based on a plurality of factors including a length of the keywords, a distance of the candidate documents from the unknown document, a similarity of the text between the unknown document and the respective candidate document, a rank of the keywords within each candidate document, and a frequency of the keyword within its respective candidate document.

System and method for labeling a document

Search results are processed using search requests, including analyzing received queries in order to provide a more sophisticated understanding of the information being sought. A concept network is generated from a set of queries by parsing the queries into units and defining various relationships between the units. From these concept networks, queries can be automatically categorized into categories, or more generally, can be associated with one or more nodes of a taxonomy. The categorization can be used to alter the search results or the presentation of the results to the user. As an example of alterations of search results or presentation, the presentation might include a list of 'suggestions' for related search query terms. As other examples, the corpus searched might vary depending on the category or the ordering or selection of the results to present to the user might vary depending on the category. Categorization might be done using a learned set of query-node pairs where a pair maps a particular query to a particular node in the taxonomy. The learned set might be initialized from a manual indication of which queries go with which nodes and enhanced has more searches are performed. One method of enhancement involves tracking post-query click activity to identify how a category estimate of a query might have varied from an actual category for the query as evidenced by the category of the post-query click activity, e.g., a particular hits of the search results that the user selected following the query. Another method involved determining relationships between units in the form of clusters and using clustering to modify the query-node pairs.

Search processing with automatic categorization of queries

Systems and methods for processing search requests are provided, including automatic generation of taxonomies and query processing using those taxonomies.

Automatic generation of taxonomies for categorizing queries and search query processing using taxonomies

In a search processing system, a concept network is generated from a set of queries by parsing the queries into units and defining various relationships between the units based in part on patterns of units that appear together in queries. Units in the concept network that have some similar characteristic(s) are grouped into superunits. For each superunit, there is a corresponding signature that defines the similar characteristic of the group. A query is processed by identifying constituent units, determining the superunit membership of some or all of the constituent units, and using that information to formulate a response to the query.

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Santa Clara, CA, United States of America

Deepa Joshi