Microsoft Technology Licensing, LLC

Blake Edward Anderson

James Charles Finger, Ii

An Yan

Nancy Ellen Jacobs

Kuldeep Karnawat

Mingyang Zhao

Thomas Dale White

Robert C Wang

Mark B Mydland

Yu Fan Chen

Keiichiro Suzuki

Jennifer J Marsman

Paul M Malolepsy

Robert Ching-Huei Wang

Reshma Girish Mehta

Methods and data structures are provided for allowing data mining with improved efficiency. During processing of a usage log (or multiple logs) for an activity, such as a usage logfile of network search activity, a common fact table is generated. The common fact table allows a plurality of auxiliary data structures to be formed from the common fact table. These auxiliary data structures are designed to allow users to submit queries against the contents of the data structure in order to investigate the data. The efficiency of access of the common fact table is improved by allowing users to access auxiliary data structures other than the auxiliary data structures that are associated with a user. Optionally, the common fact table and/or the auxiliary data structures can include dimension values that correspond to both pre-identified dimension values as well as dimension values that are identified during processing of the activity logfiles.

Distributed data warehouse

Automated analysis of user search behavior is provided. Data on user searches is maintained in a user search database. Relevance factors are determined for each search result included in a given search session where the relevance factors provide an indication of user satisfaction with particular search results included in the session. The relevance factors for each search result are analyzed by a relevance classification module for classifying each search result in terms of its relevance to an associated search query. The result of the relevance classification may assign a relevance classification and associated confidence level to each analyzed search result as to whether the search result is acceptable, unacceptable or partially acceptable relative to the search query that resulted in the search result. Relevance classifications for each analyzed search result may be stored for future use, for example, for diagnostic analysis of the operation of a given search mechanism.

Automated analysis of user search behavior

The subject disclosure pertains to systems and methods that facilitate monitoring of data collections and related data retrieval systems as well as generating recommendations for enhancing performance and utility of such systems. In aspects, the systems and methods described herein evaluate data retrieval performance by identifying unsuccessful search queries and mapping such queries to appropriate content in a data collection. To facilitate such evaluation, similar queries can be grouped and treated as equivalent. Similarity of queries can be determined based upon search terms. For example, queries whose search terms only vary syntactically can be combined in a group. Such groups can include search information related to multiple users, increasing reliability of recommendations based upon such groups. In further aspects, recommendations for adding content can be generated in conjunction with any mapping recommendations.

Search diagnostics based upon query sets

Recommendation of related assets based on user search behavior is provided. Relevance classifications assigned to search results in association with search strings that produced the search results may be used for providing a list of one or more searchable assets that may be relevant to a given search query. All pairings of two acceptable assets contained in a given search session may be marked as 'See Also' candidates. If a given pairing appears in a search session more than a configurable threshold, the assets in the pairing may be established as 'See Also' relationships. Subsequently, when a user conducts a search that produces a search result that is a member of a pairing established as a &#x201c;See Also&#x201d; relationship, the other asset in the pairing may be recommended to the searching user within the destination search result page after the user selects the search result for viewing.

Recommendation of related electronic assets based on user search behavior

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.

Forming intent-based clusters and employing same by search

Growing community of inventors

Bothell, WA, United States of America

Blake Edward Anderson