Patent application number | Description | Published |
20100011025 | TRANSFER LEARNING METHODS AND APPARATUSES FOR ESTABLISHING ADDITIVE MODELS FOR RELATED-TASK RANKING - Exemplary methods and apparatuses are provided which may be used to establish a ranking function or the like, which may be used by a search engine or other like tool to search a related-task search domain. | 01-14-2010 |
20110029517 | GLOBAL AND TOPICAL RANKING OF SEARCH RESULTS USING USER CLICKS - To estimate, or predict, the relevance of items, or documents, in a set of search results, relevance information is extracted from user click data, and relational information among the documents as manifested by an aggregation of user clicks is determined from the click data. A supervised approach uses judgment information, such as human judgment information, as part of the training data used to generate a relevance predictor model, which minimizes the inherent noisiness of the click data collected from a commercial search engine. | 02-03-2011 |
20120011112 | RANKING SPECIALIZATION FOR A SEARCH - Example methods, apparatuses, and articles of manufacture are disclosed that may be used to provide or otherwise support one or more ranking specialization techniques for use with search engine information management systems. | 01-12-2012 |
Patent application number | Description | Published |
20080208836 | Regression framework for learning ranking functions using relative preferences - A method and apparatus for determining a ranking function by regression using relative preference data. A number of iterations are performed in which to following is performed. The current ranking function is used to compare pairs of elements. The comparisons are checked against actual preference data to determine for which pairs the ranking function mis-predicted (contradicting pairs). A regression function is fitted to a set of training data that is based on contradicting pairs and a target value for each element. The target value for each element may be based on the value that the ranking function predicted for the other element in the pair. The ranking function for the next iteration is determined based, at least in part, on the regression function. The final ranking function is established based on the regression functions. For example, the final ranking function may be based on a linear combination of regression functions. | 08-28-2008 |
20080301069 | SYSTEM AND METHOD FOR LEARNING BALANCED RELEVANCE FUNCTIONS FROM EXPERT AND USER JUDGMENTS - The present invention relates to systems and methods for determining a content item relevance function. The method comprises collecting user preference data at a search provider for storage in a user preference data store and collecting expert-judgment data at the search provider for storage in an expert sample data store. A modeling module trains a base model through the use of the expert-judgment data and tunes the base model through the use of the user preference data to learn a set of one or more tuned models. A measure (B measure) is designed to evaluate the balanced performance of tuned model over expert judgment and user preference. The modeling module generates or selects the content item relevance function from the tuned models with B measure as the selection criterion. | 12-04-2008 |
20090248668 | Learning Ranking Functions Incorporating Isotonic Regression For Information Retrieval And Ranking - Embodiments of the present invention provide for methods, systems and computer program products for learning ranking functions to determine the ranking of one or more content items that are responsive to a query. The present invention includes generating one or more training sets comprising one or more content item-query pairs and determining one or more contradicting pairs in a given training sets. An optimization function to minimize the number of contradicting pairs in the training set is formulated. and modified by incorporating a grade difference between one or more content items corresponding to the query in the training set and applied to each query in the training set. A ranking function is determined based on the application of regression trees on the queries of the training set minimized by the optimization function and stored for application to content item-query pairs not contained in the one or more training sets. | 10-01-2009 |
20100082609 | SYSTEM AND METHOD FOR BLENDING USER RANKINGS FOR AN OUTPUT DISPLAY - A method and system for blending ranking for an output display includes receiving a first list of content items having a first ranking determined by first ranking parameters, the first ranking providing for a sequential ordering of the content items of the first list. A second list of content items having a second ranking determined by second ranking parameters are received, the first ranking is incompatible with the second ranking because ranking parameters are different. The first list of content items is transformed to a modified first list that maintains the order of the content items and makes the first ranking of the modified first list compatible with the second ranking of the second list. The second list and the modified first list are merged to generate a blended list for an output display utilizing the blended list. | 04-01-2010 |
Patent application number | Description | Published |
20080270404 | Using Network Traffic Logs for Search Enhancement - A method and apparatus for using network traffic logs for search enhancement is disclosed. According to one embodiment, network usage is tracked by generating log files. These log files among other things indicate the frequency web pages are referenced and modified. These log files or information from these log files can then be used to improve document ranking, improve web crawling, determine tiers in a multi-tiered index, determine where to insert a document in a multi-tiered index, determine link weights, and update a search engine index. | 10-30-2008 |
20080270484 | Using Network Traffic Logs for Search Enhancement - A method and apparatus for using network traffic logs for search enhancement is disclosed. According to one embodiment, network usage is tracked by generating log files. These log files among other things indicate the frequency web pages are referenced and modified. These log files or information from these log files can then be used to improve document ranking, improve web crawling, determine tiers in a multi-tiered index, determine where to insert a document in a multi-tiered index, determine link weights, and update a search engine index. | 10-30-2008 |
20090187566 | ASSOCIATING DOCUMENTS WITH CLASSIFICATIONS AND RANKING DOCUMENTS BASED ON CLASSIFICATION WEIGHTS - A method and apparatus for associating documents with classification values and ranking documents based on classification weights is provided. It is determined if a document is associated a classification. If the document is associated with a classification, then it is determined if a classification value, which is associated with the document, is associated with a weight. If the classification value is associated with a weight, then a rank of the document is adjusted based on the weight that is associated with the classification value. | 07-23-2009 |
20120254144 | USING NETWORK TRAFFIC LOGS FOR SEARCH ENGINE INDEX UPDATES - A method and apparatus for using network traffic logs for search enhancement is disclosed. According to one embodiment, network usage is tracked by generating log files. These log files among other things indicate the frequency web pages are referenced and modified. These log files or information from these log files can then be used to improve document ranking, improve web crawling, determine tiers in a multi-tiered index, determine where to insert a document in a multi-tiered index, determine link weights, and update a search engine index. | 10-04-2012 |
20120271842 | LEARNING RETRIEVAL FUNCTIONS INCORPORATING QUERY DIFFERENTIATION FOR INFORMATION RETRIEVAL - The system and method of the present invention allows for the determination of the relevance of a content item to a query through the use of a machine learned relevance function that incorporate query differentiation. A method for selecting a relevance function to determine a relevance of a query-content item pair comprises generating a training set comprising one or more content item-query pairs. Content item-query pairs in the training set are collectively used to determine the relevance function by minimizing a loss function according to a relevance score adjustment function that accounts for query differentiation. The monotocity of relevance score adjustment function allows the trained relevance function to be directly applied to new queries. | 10-25-2012 |