Abstract: Described herein are technologies pertaining to determining which search engine results page (SERP), from a plurality of SERPs, is preferable to a user. A query is received, and multiple SERPS are retrieved based upon the query, wherein the multiple SERPs are generated independently from one another. Values of features of the query and the multiple SERPs are obtained, and a determination as to which of the SERPs from the multiple SERPs is preferable to the user is made based upon the values of the features. The SERP determined to be preferable over other SERPs in the multiple SERPs is presented to the user.

Abstract: Methods for updating an information retrieval system are disclosed. In one embodiment, search terms affiliated with mappings or associations that represent a connection of relevancy between a query and an asset are pushed as content updates to a client system (e.g., as new updates or utilized to replace older data). The search terms are inserted (e.g., inserted as metadata) into corresponding content (the content associated with the asset). In this manner, content-searching data can be updated (e.g., remotely updated) as frequently as desired, even periodically, or selectively as new manually and/or automatically derived data becomes available. In another embodiment, the update data is already built into the content when it is delivered to a client machine. Other disclosed embodiments pertain to methods for generating a data mining classification model that is a blended representation of associations (e.g., query-asset associations) having different characteristics and/or different originating sources.

Abstract: A computer-implemented method includes training a data mining classification model to statistically account for query-to-asset associations. This data mining classification model can be utilized as a component of an information retrieval system.

Abstract: A system analyzes data from a search engine. A User Search Bundler analyzes User Searches groups similar User Searches into User Search Bundles, and an Intent Processor produces Intents based on the User Search Bundles. A Factor Generator considers User Searches and related information to produce Factors, where each Factor is with regard to a particular Result from a set of Search Results. A Relevance Classifier receives the Factors and operates based thereon to produce a Judgment for each Result. A Metric Generator produces Metrics based on the Factors and the Judgments, and, a data synthesizer formats extracted data into databases.