Using Variation in User Interest to Enhance the Search Experience

• US 20090327270A1
• Filed: 06/27/2008
• Published: 12/31/2009
• Est. Priority Date: 06/27/2008
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Teevan, Jamie B., Dumais, Susan T., Horvitz, Eric J., Liebling, Daniel J.
• Application Number:
  US12/163,561
• Publication Number:
  US 20090327270A1
• 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 16/9535 Search customisation based on user profiles and personalisation

Abstract

Searches can be enhanced by custom-tailoring results based on a consideration of the variability of the goals of a search given a query. In an example embodiment, a system to enhance searching includes a search interface, a search-goal variability determiner, and a search experience enhancer. The search interface accepts a query from a user as input for a search. The variability determiner determines the variability in user interest (e.g., goals) for the query. The measure of variability in user interest may reflect the degree of variation in the goals of different users or groups of users for the query. The search experience enhancer enhances a search experience for the user responsive to the variability in user interest (e.g., in terms of search goals).

Claims

1. A device-implemented method to enhance searching, the method comprising acts of:

accepting a query from a user as input for a search;

determining a variability in user interest for the query, the variability in user interest reflecting an amount that interests of different users for different search results vary for the query;

enhancing a search experience for the user by incorporating a degree of personalization into the search responsive to the variability in user interest; and

presenting a set of search results in accordance with the enhanced search experience.

2. The method as recited in claim 1, wherein the act of enhancing comprises:

increasing the degree of personalization that is incorporated into the search responsive to increases in the variability in user interest.

3. The method as recited in claim 2, wherein the act of determining comprises:

determining a potential for personalization amount that reflects the amount that interests of different users for different search results vary for the query.

4. The method as recited in claim 3, wherein the act of determining further comprises:

building at least one user interest score matrix based on multiple interest scores; and

determining the potential for personalization amount at one or more group sizes to produce at least part of a potential for personalization curve responsive to the at least one user interest score matrix.

5. A system to enhance searching, the system comprising:

a search interface to accept a query from a user as input for a search;

a variability determiner to determine a variability in user interest for the query, the variability in user interest reflecting an amount that interests of different users for different search results vary for the query; and

a search experience enhancer to enhance a search experience for the user responsive to the variability in user interest.

6. The system as recited in claim 5, wherein the variability determiner comprises:

an implicit variability measurer to measure the variability in user interest with one or more implicit indications, the implicit variability measurer including an implicit potential for personalization curve constructor or a click entropy calculator;

wherein the implicit potential for personalization curve constructor is to construct a potential for personalization curve that represents the variability in user interest at different group sizes, and the click entropy calculator is to calculate a click entropy for the query based on a probability that individual search results are clicked for the query.

7. The system as recited in claim 5, wherein the variability determiner comprises:

an implicit variability measurer to measure the variability in user interest with one or more implicit indications, the implicit variability measurer including a behavior-based variability measurer;

wherein the behavior-based variability measurer is to measure the variability in user interest based on at least one observable user interaction behavior with a search results listing that is presented for the query.

8. The system as recited in claim 5, wherein the variability determiner comprises:

an implicit variability measurer to measure the variability in user interest with one or more implicit indications, the implicit variability measurer including a content-based variability measurer;

wherein the content-based variability measurer is to measure the variability in user interest based on content by comparing a user profile to search results produced for the query.

9. The system as recited in claim 5, wherein the variability determiner comprises:

a variability predictor that includes a query feature evaluator to evaluate at least one feature of the query, wherein the query feature evaluator is to predict the variability in user interest based on the at least one feature of the query.

10. The system as recited in claim 5, wherein the variability determiner comprises:

a variability predictor that includes a search result set feature evaluator to evaluate at least one feature of a search results set produced for the search, wherein the search result set feature evaluator is to predict the variability in user interest based on the at least one feature of the search results set.

11. The system as recited in claim 5, wherein the variability determiner comprises:

a variability predictor that includes a history feature evaluator to evaluate at least one historical feature derived from one or more previous search submissions of the query, wherein the history feature evaluator is to predict the variability in user interest based on the at least one historical feature.

12. The system as recited in claim 5, wherein the search experience enhancer comprises a first search ranking scheme and a second search ranking scheme;

and wherein the search experience enhancer is to incorporate the first search ranking scheme and the second search ranking scheme into the search responsive to the variability in user interest when the system is ranking a set of search results produced for the search.

13. The system as recited in claim 12, wherein the first search ranking scheme comprises a personalized search ranking scheme, and the second search ranking scheme comprises a non-personalized search ranking scheme;

and wherein the search experience enhancer is to combine the first search ranking scheme and the second search ranking scheme in accordance with a linear combination mechanism responsive to the variability in user interest.

14. The system as recited in claim 5, wherein the variability determiner comprises:

a noise compensator to compensate for noise that permeates implicit user interest variability indications;

wherein the noise compensator is to compensate for result set changes for the query over time, for task purpose differences among queries, or for result quality differences.

15. A device-implemented method to enhance searching, the method comprising acts of:

accepting a query from a user as input for a search;

determining a variability in user interest for the query, the variability in user interest reflecting an amount that interests of different users for different search results vary for the query; and

enhancing a search experience for the user responsive to the variability in user interest.

16. The method as recited in claim 15, wherein the act of determining comprises:

explicitly measuring the variability in user interest for the query;

implicitly measuring the variability in user interest for the query;

or

predicting the variability in user interest for the query.

17. The method as recited in claim 15, wherein the act of enhancing comprises:

selecting at least one search ranking scheme for performing the search responsive to the variability in user interest;

setting one or more search ranking parameters for search results of the search responsive to the variability in user interest;

adjusting a search results presentation responsive to the variability in user interest;

or

presenting a user dialog to disambiguate the query responsive to the variability in user interest.

18. The method as recited in claim 15, wherein the act of enhancing comprises:

increasing a degree of personalization incorporated into the search as the variability in user interest increases.

19. The method as recited in claim 15, wherein the act of determining comprises constructing a potential for personalization curve by:

collecting respective interest scores from multiple users for respective search results for the query;

selecting at least one measure of quality for ranking search results; and

constructing the potential for personalization curve based on the interest scores and the at least one measure of quality.

20. The method as recited in claim 19, wherein a gap between an optimal flat potential for personalization curve and the constructed potential for personalization curve defines a potential for personalization amount, the potential for personalization amount reflecting an amount that the search for the query may be enhanced by incorporating a degree of personalization.