Patent application number | Description | Published |
20090006356 | CHANGING RANKING ALGORITHMS BASED ON CUSTOMER SETTINGS - Search term ranking algorithms can be generated and updated based on customer settings, such as where a ranking algorithm is modeled as a combination function of different ranking factors. An end user of a search system provides personalized preferences for weighted attributes, generally or for a single instance of the query. The user also can indicate the relative importance of one or more ranking factors by specifying different weights to the factors. Ranking factors can specify document attributes, such as document title, document body, document page rank, etc. Based on the attribute weights and the received user query, a ranking algorithm function will produce the relevant value for each document corresponding to the user preferences and personalization configurations. | 01-01-2009 |
20090006359 | AUTOMATICALLY FINDING ACRONYMS AND SYNONYMS IN A CORPUS - Acronym and synonym pairs can be identified and retrieved automatically in a corpus and/or across an enterprise based on customer settings globally or for a single instance. Possible acronym and synonym term pairs can be identified using a rule such as a heuristic, user-defined rule. Rules selected by the user can be used to rank acronym and synonym pairs using factors such as occurrence frequency and maximum term length. A rule interpreter engine executes the user defined rule set to properly identify and retrieve the user selected acronym and synonym pairs through the utilization of a shallow pause read step. Finally, the user selected acronym and synonym pairs are ranked according to the user preferences, and can be displayed or held for subsequent use in searching. | 01-01-2009 |
20090006360 | SYSTEM AND METHOD FOR APPLYING RANKING SVM IN QUERY RELAXATION - An enterprise-wide query relaxative support vector machine ranking algorithm approach provides enhanced functionality for query execution in a heterogeneous enterprise environment. Improved query results are obtained by adjusting ranking functions using machine learning methods to automatically train ranking functions. The improved query results are obtained using a list of document-query pairs that are modeled as a binary classification training problem, combination function which requires ranking and learning functions to be implemented representing document attributes and metadata utilizing query relaxation techniques and adjusted ranking functions. Machine learning methods implement user feedback to automatically train ranking functions. | 01-01-2009 |
20100185611 | RE-RANKING SEARCH RESULTS FROM AN ENTERPRISE SYSTEM - A flexible and extensible architecture allows for secure searching across an enterprise. Such an architecture can provide a simple Internet-like search experience to users searching secure content inside (and outside) the enterprise. The architecture allows for the crawling and searching of a variety of sources across an enterprise, regardless of whether any of these sources conform to a conventional user role model. The architecture further allows for security, recency, or other attributes to be submitted at query time, for example, in order to re-rank query results from enterprise resources. The user query also can be transformed to provide for dynamic querying that provides for a more current result list than can be obtained for static queries. | 07-22-2010 |
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 |
20110093459 | Incorporating Recency in Network Search Using Machine Learning - In one embodiment, access a set of recency ranking data comprising one or more recency search queries and one or more recency search results, each of the recency search queries being recency-sensitive with respect to a particular time period and being associated with a query timestamp representing the time at which the recency search query is received at a search engine, each of the recency search results being generated by the search engine for one of the recency search queries and comprising one or more recency network resources. Construct a plurality of recency features from the set of recency ranking data. Train a first ranking model via machine learning using at least the recency features. | 04-21-2011 |
20110191313 | Ranking for Informational and Unpopular Search Queries by Cumulating Click Relevance - One embodiment accesses a search query and one or more sets of clicked network resources corresponding to the search query; determines a classifier model that represents the sets of clicked network resources that each satisfy the information need of one of the users and one or more subsets of the sets of clicked network resources that each do not satisfy the information need of one of the users; computes a probability value for each clicked network resource from each of the sets of clicked network resources using the classier model, wherein the probability value represents a likelihood that, after clicking on the corresponding network resource, the particular one of the users conducting the corresponding particular one of the search sessions ends the search session; and forms a set of features comprising the probability values computed for network resources from the search sessions. | 08-04-2011 |
20110231380 | SESSION BASED CLICK FEATURES FOR RECENCY RANKING - In one embodiment, access one or more query chains, wherein each one of the query chains comprises two or more search queries, {q | 09-22-2011 |
20110231390 | SESSION BASED CLICK FEATURES FOR RECENCY RANKING - In one embodiment, access one or more query-resource pairs, wherein for each one of the query-resource pairs comprising one of one or more search queries and one of one or more network resources, the one search query is recency-sensitive with respect to a particular time period, and the one network resource is identified for the one search query, and a resource-view count and a resource-click count associated with each one of the query-resource pairs; and construct one or more first click features using the resource-view counts and the resource-click counts associated with the query-resource pairs. To construct one of the first click features in connection with one of the query-resource pairs comprises determine a only-resource-click count associated with the one query-resource pair; and calculate a ratio between the only-resource-click count and the resource-view count associated with the one query-resource pair as the one first click feature. | 09-22-2011 |
20110258184 | CHANGING RANKING ALGORITHMS BASED ON CUSTOMER SETTINGS - Search term ranking algorithms can be generated and updated based on customer settings, such as where a ranking algorithm is modeled as a combination function of different ranking factors. An end user of a search system provides personalized preferences for weighted attributes, generally or for a single instance of the query. The user also can indicate the relative importance of one or more ranking factors by specifying different weights to the factors. Ranking factors can specify document attributes, such as document title, document body, document page rank, etc. Based on the attribute weights and the received user query, a ranking algorithm function will produce the relevant value for each document corresponding to the user preferences and personalization configurations. | 10-20-2011 |
20110265189 | RE-RANKING SEARCH RESULTS FROM AN ENTERPRISE SYSTEM - A flexible and extensible architecture allows for secure searching across an enterprise. Such an architecture can provide a simple Internet-like search experience to users searching secure content inside (and outside) the enterprise. The architecture allows for the crawling and searching of a variety of sources across an enterprise, regardless of whether any of these sources conform to a conventional user role model. The architecture further allows for security, recency, or other attributes to be submitted at query time, for example, in order to re-rank query results from enterprise resources. The user query also can be transformed to provide for dynamic querying that provides for a more current result list than can be obtained for static queries. | 10-27-2011 |
20120272304 | CRAWLING SECURE DATA SOURCES - It is desirable to provide a secure search mechanism to provide for searching over any and all content, such as across an enterprise. A secure search, however, requires access to the secure content repositories holding the data to be searched. In some cases the credentials required to crawl a repository may be extremely sensitive, or the user may be reluctant or unwilling to store user identification information in memory or on disk for any longer than is absolutely necessary. An approach is provided that allows a user or an administrator to provide security credentials to be stored and used only during a crawl, and to erase the credentials from the system when the crawl is complete. | 10-25-2012 |
20130173582 | INDEXING SECURE ENTERPRISE DOCUMENTS USING GENERIC REFERENCES - A web crawler indexes documents including information about document contents and metadata including information such as a URL. However, some applications rely on URL's that change frequently or are constructed to include user information so that the contents retrieved is customized to the user. An approach is provided for storing generic URL's in an index at crawl time, which are customized for the user at search time. A callback mechanism may be used to dynamically transform the generic URL into a URL that is specific to the user issuing the query and/or includes current information that may change frequently. In this way, when the query or search results are returned to the user, the user receives links that are active and valid for that particular user, directing the user to the appropriate site, application, etc. without requiring continuous updating of a very large index. | 07-04-2013 |
20140358913 | HIERARCHICAL ENTITY INFORMATION FOR SEARCH - A fast browsing architecture for exploring hierarchical lists of entities through a search user interface. A graphical UI operates to handle the hierarchical lists and sub-lists in different ways for different scenarios such as a hierarchical level is zero (only one list of entities associated with a query and the list cannot be further drilled down), a second scenario where the hierarchical level is one (a list of entities associated with the query and these entities can be further drilled down to a number of sub-lists) and the sub-lists cannot be further drilled down, and a third scenario where the hierarchical level is more than one (a list of entities associated with the query and these entities can be further drilled down to a number of sub-lists), sub-lists can be further drilled down to a number of lists, until there is no more drill down lists to be found. | 12-04-2014 |