Abstract: A method is provided for analyzing a plurality of search sessions to identify intent-based clusters therein. Each session comprises at least one received query from a user and a corresponding set of returned search results, and each set of search results includes or refers to at least one piece of content. Each cluster represents a group of similar search sessions that are perceived as representing a common purpose and that can be mapped to a common set of search results. In the method, for each search session, each received query thereof, the corresponding set of search results, and whether any particular piece of content of the search results was acceptable to the user as responsive to the corresponding search session are identified. Thereafter, search sessions are grouped into clusters.

Abstract: The subject invention leverages data logging of responses to diagnostic reports to provide data that can be mined for diagnostic report quality information. Instances of the subject invention provide an initial diagnostic report assessment means to facilitate review by an entity. The entity's responses to the sorted diagnostic reports are logged unobtrusively to create diagnostic report quality data. This data is then analyzed by an analysis means that can then adjust the assessment means to improve its performance. In this manner, the performance of the assessment means is increased while reducing the workload of the entity reviewing the diagnostic reports. Other instances of the subject invention facilitate to increase the performance of a diagnostic report generating means as well. Instances of the subject invention can also employ machine learning techniques to facilitate in analyzing the quality data and/or in assessing the diagnostic reports.

Abstract: Systems and methods for measuring and improving search result relevance based on user satisfaction are disclosed. An event log is preferably scanned to determine the start and the end of a session. User behaviors within the session are preferably identified. User satisfaction is preferably estimated based on the user behaviors. User satisfaction is preferably a measure of the user's perception of the extent to which a result correlates to his intent based on his behaviors. User satisfaction is preferably estimated to be one of three satisfaction levels: satisfied, partially-satisfied, and dissatisfied. Symptoms, which are indications of a need for improvement within a search system, are preferably observed based on the user behaviors and the user satisfaction. Diagnostics, which are conditions that prevent a search system from performing optimally, are preferably derived based on the symptoms.

Abstract: Context-based user feedback is gathered regarding searches performed on a search mechanism. The search mechanism is monitored for user behavior data regarding an interaction of a user with the search mechanism. The response data provided by the search mechanism is also monitored. Context data (describing the search) and user feedback data (the user's feedback on the search—either explicit or implicit) are determined. This can be used, for example, to evaluate a search mechanism or to check a relevance model.