| 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 |
