Patent application number | Description | Published |
20090106221 | Ranking and Providing Search Results Based In Part On A Number Of Click-Through Features - Embodiments are configured to provide information based on a user query. In an embodiment, a system includes a search component having a ranking component that can be used to rank search results as part of a query response. In one embodiment, the ranking component includes a ranking algorithm that can use one or more click-through features to rank search results which may be returned in response to a query. Other embodiments are available. | 04-23-2009 |
20090106223 | ENTERPRISE RELEVANCY RANKING USING A NEURAL NETWORK - A neural network is used to process a set of ranking features in order to determine the relevancy ranking for a set of documents or other items. The neural network calculates a predicted relevancy score for each document and the documents can then be ordered by that score. Alternate embodiments apply a set of data transformations to the ranking features before they are input to the neural network. Training can be used to adapt both the neural network and certain of the data transformations to target environments. | 04-23-2009 |
20090182723 | RANKING SEARCH RESULTS USING AUTHOR EXTRACTION - Architecture that extracts author information from general documents and uses the author information for search results ranking. The architecture performs automatic author value extraction and makes the extracted value available at index time for subsequent use at query processing and results ranking. Machine learning (e.g., a perceptron algorithm) is employed and a set of input features for the perceptron algorithm utilized for author value extraction. The extracted author value is converted into a feature for input a ranking function for generating a ranking score for each document. The input features can also be weighted according to weighting criteria. | 07-16-2009 |
20090319505 | TECHNIQUES FOR EXTRACTING AUTHORSHIP DATES OF DOCUMENTS - Various technologies and techniques are disclosed for calculating authorship dates for a document. A portion of a document to select to look for possible authorship dates is determined. The possible authorship dates are extracted from the portion of the document. A revised authorship date of the document is generated using a neural network. The revised authorship date is returned to an application or process that requested the date. | 12-24-2009 |
20100169324 | RANKING DOCUMENTS WITH SOCIAL TAGS - Technologies are described herein for ranking documents with social tags. A number ranking feature containing a number of times a document was tagged is received. A textual property ranking feature containing a union of each social tag associated with the document is also received. The number ranking feature is transformed into a static input value. Further, the textual property ranking feature is transformed into a dynamic input value. A document rank for the document is determined by inputting the static input value and/or the dynamic input value into a ranking function. | 07-01-2010 |
20100174712 | EXPERTISE RANKING USING SOCIAL DISTANCE - Tools and techniques for expertise ranking using social distance are provided. These tools may receive search queries from users, and extract from these search queries record identifiers associated with the users. In addition, the tools may extract query strings from the search queries. In connection with processing these queries, the tools may identify other users associated with a given user, with some of these other users being first-level colleagues of a given user, and some of these other users being second-level colleagues. The tools may identify documents within a search store that are associated with the other users, and may search these documents for any occurrences of the query string. In turn, results of the search may be ranked based on a social distance between the user and the other users, with the social distance indicating whether the other users are first-level or second-level colleagues of the user. | 07-08-2010 |
20110125732 | INTERNAL RANKING MODEL REPRESENTATION SCHEMA - A markup language schema utilized to represent internal ranking models. In one implementation, the schema developed utilizes XML (extensible markup language) for internal ranking model representation. Other markups languages can be employed. | 05-26-2011 |
20110137893 | CUSTOM RANKING MODEL SCHEMA - A customizable ranking model of a search engine using custom ranking model configuration and parameters of a pre-defined human-readable format. The architecture can employ a markup language schema to represent the custom ranking model. In one implementation, the schema developed utilizes XML (extensible markup language) for representing the custom ranking model. Weights for dynamic and static relevance ingredients can be altered per ranking model and new relevance ingredients can be added. Additionally, features are provided for improving relevance such as adding terms to a thesaurus for synonym expansion, for example, the ability to deal with single terms either as compounds, and/or using custom word breaking rules. | 06-09-2011 |
20110313548 | Event Prediction Using Hierarchical Event Features - Event prediction using hierarchical event features is described. In an embodiment a search engine monitors search results presented to users and whether users click on those search results. For example, features describing the search result events are universal resource locator prefix levels which are inherently hierarchically related. In an embodiment a graphical data structure is created and stored and used to represent the hierarchical relationships between features. An online training process is used in examples which enables knowledge to be propagated through the graphical data structure according to the hierarchical relations between features. In an example, the graphical data structure is used to predict whether a user will click on a search result and those predictions are used by the search engine to rank search results for future searches. In another example the events are advertisement impressions and the predictions are used by an online advertisement system. | 12-22-2011 |
20130006900 | Event Prediction Using Hierarchical Event Features - Event prediction using hierarchical event features is described. In an embodiment a search engine monitors search results presented to users and whether users click on those search results. For example, features describing the search result events are universal resource locator prefix levels which are inherently hierarchically related. In an embodiment a graphical data structure is created and stored and used to represent the hierarchical relationships between features. An online training process is used in examples which enables knowledge to be propagated through the graphical data structure according to the hierarchical relations between features. In an example, the graphical data structure is used to predict whether a user will click on a search result and those predictions are used by the search engine to rank search results for future searches. In another example the events are advertisement impressions and the predictions are used by an online advertisement system. | 01-03-2013 |
20130110816 | Default Query Rules | 05-02-2013 |
20130110860 | USER PIPELINE CONFIGURATION FOR RULE-BASED QUERY TRANSFORMATION, GENERATION AND RESULT DISPLAY | 05-02-2013 |
20130191371 | USING POPULAR QUERIES TO DECIDE WHEN TO FEDERATE QUERIES - A query received from a user is directed to a particular search application (e.g. an Enterprise search portal) that is associated with a result source from which to retrieve results. The received query may be federated to additional result sources when the received query is determined to be a popular query in a result source. Query logs associated with the additional result sources are analyzed to determine when a query is popular as compared to the original result source. The query may be altered before being executed that uses one or more of the additional result sources. When the query (altered/unaltered) is determined to be popular for any of the additional result sources as compared to the original result source, the query is executed using that additional result source. | 07-25-2013 |