Analyzing operational and other data from search system or the like

• US 20060085401A1
• Filed: 10/20/2004
• Published: 04/20/2006
• Est. Priority Date: 10/20/2004
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Anderson, Blake E., Shimizu, Takeshi, Zhang, Ying, Karnawat, Kuldeep, Finger, James C., White, Thomas D., Malolepsy, Paul M., Marsman, Jennifer J., Mydland, Mark B., Behr, Frederic H. Jr.
• Application Number:
  US10/969,567
• Publication Number:
  US 20060085401A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/337 Profile generation, learning or modification
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 2216/03 Data mining

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.

Claims

1. A system for analyzing data from a search engine, the search engine generating a set of Search Results based on a Query String received from a requesting user, the Query String and the Search Results collectively comprising a User Search, the Search Results including at least one Result, each Result referencing a particular item of content believed to be relevant to the Query String, whereby a series of related User Searches comprises a Session, the search engine storing each User Search and related information, the system comprising:

a User Search Bundler (USB) analyzing User Searches to find similar ones of such User Searches and group such similar User Searches into User Search Bundles;

an Intent Processor (IP) producing Intents based on User Search Bundles from the USB, each Intent being a group of one or more Sessions that are believed to be related to each other;

a Factor Generator (FG) considering User Searches and related information to produce Factors, each Factor being with regard to a particular Result from a set of Search Results, each Factor relating to one or more Events, each Event being a piece of information relating to an act that a querying user performed;

a Relevance Classifier (RC) receiving the Factors as generated by the FG for each Result and operating based thereon to produce a Judgment for the Result, the Judgment representing a determination of how the user judged the Result upon deciding to access same from the Search Results;

a Metric Generator (MG) producing Metrics based on the Factors as generated by the FG and the Judgments as produced by the RC, each Metric being a measurement relating to a Result, a User Search, or a Session; and

a data synthesizer (DS) extracting data generated by the USB, IP, FG, RC, and MG, formatting the extracted data into one or more databases, and storing the databases in a library, whereby the data can be reviewed and aggregated to provide feedback or generate reports.

2. The system of claim 1 wherein the search engine stores each Query String and the corresponding Search Results and related information in a data warehouse and in a normalized form, the system further comprising a de-normalizer retrieving the normalized data from the data warehouse, normalizing same, and storing the normalized data in a data store.

3. The system of claim 1 wherein the USB analyzes the User Searches for at least one of similarity of Query Strings and similarity of Search Results.

4. The system of claim 1 wherein each Event includes a time when the user performed at least one of selecting and closing a particular Result, and wherein the FG computes a “

Dwell Time”

Factor that represents a length of time a user viewed a Result, the Dwell Time Factor being based on a difference in time between when the user selected and closed the Result, each as represented by a corresponding time-stamped Event.

5. The system of claim 1 wherein the RC produces a Judgment comprising at least one of an “

Accept”

Judgment, an “

Explore”

Judgment, and a “

Reject”

Judgment and a corresponding value indicative of a confidence for how likely the Judgment is correct.

6. The system of claim 1 further comprising a Relevance Classifier Trainer receiving Explicit Judgment Factors from the FG and generating the RC based thereon, each Explicit Judgment Factor representing explicit feedback from the user regarding the corresponding Result, the RCT learning from the Explicit Judgment Factors what Factors imply which Judgments and based thereon generating the RC.

7. The system of claim 1 wherein the MG produces with regard to a Result at least one of:

a Position Metric regarding how the user was judged to have ranked the Result;

a Relevance Position Metric regarding how the Result was positioned within the Search Results; and

a Mis-ranked Result Metric regarding how ‘

far’

the Result was from where same should have been, based on the Position Metric and the Relevance Position Metric.

8. The system of claim 1 wherein the IP determines a relationship value between Sessions by locating common Results across Sessions and common Query Terms across Sessions based on reviewed User Search Bundles, and ascertains a Strength of Commonality when such common Results are found, such Strength of Commonality representing how likely two Sessions are to be related to each other by having a common purpose, the IP bundling Session pairs having a Strength of Commonality above a determined threshold into an Intent.

9. The system of claim 1 wherein the DS formats the extracted data into a relational database.

10. A method for analyzing data from a search engine, the search engine generating a set of Search Results based on a Query String received from a requesting user, the Query String and the Search Results collectively comprising a User Search, the Search Results including at least one Result, each Result referencing a particular item of content believed to be relevant to the Query String, whereby a series of related User Searches comprises a Session, the search engine storing each User Search and related information, the method comprising:

analyzing User Searches to find similar ones of such User Searches and group such similar User Searches into User Search Bundles;

producing Intents based on User Search Bundles from the USB, each Intent being a group of one or more Sessions that are believed to be related to each other;

considering User Searches and related information to produce Factors, each Factor being with regard to a particular Result from a set of Search Results, each Factor relating to one or more Events, each Event being a piece of information relating to an act that a querying user performed;

receiving the Factors as generated for each Result and operating based thereon to produce a Judgment for the Result, the Judgment representing a determination of how the user judged the Result upon deciding to access same from the Search Results;

producing Metrics based on the Factors and the Judgments, each Metric being a measurement relating to a Result, a User Search, or a Session; and

extracting data including the User Search Bundles, the Intents, the Factors, the Judgments, and the Metrics, formatting the extracted data into one or more databases, and storing the databases in a library, whereby the data can be reviewed and aggregated to provide feedback or generate reports.

11. The method of claim 10 comprising storing each Query String and the corresponding Search Results and related information in a data warehouse and in a normalized form, and further comprising retrieving the normalized data from the data warehouse, normalizing same, and storing the normalized data in a data store.

12. The method of claim 10 comprising analyzing the User Searches for at least one of similarity of Query Strings and similarity of Search Results.

13. The method of claim 10 wherein each Event includes a time when the user performed at least one of selecting and closing a particular Result, the method comprising computing a “

Dwell Time”

Factor that represents a length of time a user viewed a Result, the Dwell Time Factor being based on a difference in time between when the user selected and closed the Result, each as represented by a corresponding time-stamped Event.

14. The method of claim 10 comprising producing a Judgment comprising at least one of an “

Accept”

Judgment, an “

Explore”

Judgment, and a “

Reject”

Judgment and a corresponding value indicative of a confidence for how likely the Judgment is correct.

15. The method of claim 10 further comprising receiving Explicit Judgment Factors and generating a Relevance Classifier (RC) based thereon, the RC receiving the Factors as generated for each Result and operating based thereon to produce the Judgment for the Result, each Explicit Judgment Factor representing explicit feedback from the user regarding the corresponding Result such that what Factors imply which Judgments can be learned based on such Explicit Judgment Factors.

16. The method of claim 10 comprising producing with regard to a Result at least one of:

a Position Metric regarding how the user was judged to have ranked the Result;

a Relevance Position Metric regarding how the Result was positioned within the Search Results; and

a Mis-ranked Result Metric regarding how ‘

far’

the Result was from where same should have been, based on the Position Metric and the Relevance Position Metric.

17. The method of claim 10 comprising determining a relationship value between Sessions by locating common Results across Sessions and common Query Terms across Sessions based on reviewed User Search Bundles, and ascertaining a Strength of Commonality when such common Results are found, such Strength of Commonality representing how likely two Sessions are to be related to each other by having a common purpose, Session pairs having a Strength of Commonality above a determined threshold being bundled into an Intent.

18. The method of claim 10 comprising formatting the extracted data into a relational database.