Search query processing to provide category-ranked presentation of search results

• US 6,963,867 B2
• Filed: 03/31/2003
• Issued: 11/08/2005
• Est. Priority Date: 12/08/1999
• Status: Expired due to Term

• Current Assignee: A9.com Incorporated (Amazon.com, Inc.)
• Original Assignee: A9.com Incorporated (Amazon.com, Inc.)
• Inventors: Ford, James L., Scofield, Christopher L., Bowman, Dwayne E., Ortega, Ruben E.
• Primary Examiner(s): MOFIZ, APU M
• Application Number:
  US10/404,992
• Publication Number:
  US 20030195877A1
• Time in Patent Office: 953 Days
• Field of Search: 707/3, 707/4, 707/5, 707/6, 707/10, 707/104.1, 235/375, 235/472.02, 704/275, 705/10, 705/14, 705/26, 709/203, 725/112
• US Class Current: 707/752
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06F 16/358 Browsing; Visualisation therefor
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching
• CPC Class Codes: Y10S 707/99937 Sorting

Abstract

A search engine system displays the results of a multiple-category search according to levels of relevance of the categories to a user'"'"'s search query. A query server receives a search query from a user and identifies, within each of multiple item categories, a set of items that satisfy the query. The sets of items are used to generate, for each of the multiple categories, a score that reflects a level significance or relevance of the category to the search. The scores may be based, for example, on the number of hits within each category relative to the total number of items in that category, the popularity levels of items that satisfy the query, a personal profile of the user, or a combination thereof. The categories are then presented to the user, together with the most relevant items within each category, in the order of highest to lowest category relevance.

Claims

1. A computer-implemented method of assisting users in locating items that are arranged within a database system by category, the method comprising:

monitoring user actions performed with respect to specific items in the database system to generate user activity data;

calculating item scores for specific items in the database system, wherein an item score for an item is dependent upon the user activity data associated with that item, and wherein calculating the item scores comprises automatically giving greater weight to user activity data associated with a most recent time period than to user activity data associated with earlier time periods;

receiving a search query specified by a user;

identifying, within each of multiple categories of the database system, items that are responsive to the search query (“

responsive items”

);

for each of the multiple categories, calculating a respective category score based at least in-part on the item scores of responsive items in that category; and

determining an order in which to present the multiple categories to the user such that the order is dependent upon the category scores.

2. The method of claim 1, further comprising outputting identifiers of the multiple categories arranged in said order, together with identifiers of corresponding responsive items within each of the multiple categories, for presentation to the user.

3. The method of claim 2, wherein the step of outputting identifiers of the multiple categories arranged in said order comprises generating a search results page in which each of the multiple categories is listed together with a selected subset of the responsive items falling within that category.

4. A search results page generated according to the method of claim 3 and represented within a computer.

5. The method of claim 1, wherein the category scores are calculated based further upon a profile of the user such that the order in which the categories are presented is personalized for the user.

6. The method of claim 1, wherein the category score for a given category is calculated based on the item scores of a most highly ranked subset of the responsive items within the given category.

7. The method of claim 1, wherein the category score for a given category is based further upon a number of responsive items falling within the given category relative to a total number of items falling within the given category.

8. The method of claim 1, wherein the user activity data reflects item viewing events.

9. The method of claim 8, wherein the user activity data further reflects the following types of user actions:

selections of items to add to a shopping cart, and item purchases.

10. The method of claim 1, wherein the item scores reflect current popularity levels of items among a population of users of the database system.

11. The method of claim 1, wherein each category score represents a predicted level of significance of the respective category to the search query.

12. A server system that embodies the method of claim 1.

13. The method of claim 1, wherein the items are products that are available for purchase.

14. The method of claim 1, wherein automatically giving greater weight to user activity data associated with a most recent time period comprises using a sliding window to select a set of user activity data to be taken into consideration.

15. The method of claim 1, wherein each item score corresponds to a keyword-item pair, and reflects a degree to which a keyword and an item are associated as determined from an automated analysis of the user activity data.

16. A computer readable medium which stores a computer program that embodies the method of claim 1.

17. A computer-implemented method of assisting users in locating items that are arranged by category within an electronic catalog, the method comprising:

monitoring actions performed by a plurality of users, including user actions performed with respect to items in the electronic catalog, to generate user activity data;

calculating popularity scores for particular items in the catalog using said user activity date, said popularity scores being calculated automatically based primarily or exclusively on user activity data collected over a most recent time period, such that the popularity scores correspond to current preferences of users;

receiving a search query specified by a user, and identifying a set of items in the electronic catalog that are responsive to the search query (“

responsive items”

), including items from multiple categories;

selecting an order in which to present the multiple categories to the user such that the order is dependent upon the popularity scores of responsive items, said order reflecting predicted levels of relevance of the categories to the search query; and

generating a search results display in which the multiple categories are presented in said order together with corresponding responsive items.

18. The method as in claim 17, wherein the order in which the categories are presented is selected based further upon a number of responsive items falling within a given category relative to a total number of items in the given category.

19. The method of claim 17, wherein the order in which the categories are presented is based further upon a profile of the user such that the order is personalized for the user.

20. A search results display generated according to the method of claim 17 and represented within a computer.

21. A server system that embodies the method of claim 17.

22. The method of claim 17, wherein calculating the popularity scores for particular items comprises a giving greater weight to user activity data associated with a most recent time period than to user activity data associated with earlier time periods.

23. The method of claim 17, wherein calculating the popularity scores for particular items comprises applying a sliding window to user activity data collected over time.

24. The method of claim 17, wherein each item score corresponds to a keyword-item pair, and reflects a degree to which a keyword and an item are associated as determined from an automated analysis of the user activity data.

25. The method of claim 17, wherein the items are products that are available for purchase, and the categories represent groupings of products.

26. A computer-implemented method of processing search queries, the method comprising:

receiving a search query specified by a user;

identifying a plurality of item categories that each include one or more items that are responsive to the search query;

for each of the plurality of item categories, determining a respective category score, each category score being dependent upon results of an automated analysis of user activity data associated with the respective item category, said user activity data reflecting selection actions of each of a plurality of users, wherein the automated analysis gives greater weight to user activity data associated with a most recent time period than to user activity data associated with earlier time periods, so that the category scores reflect current preferences of users; and

determining an order in which to present the multiple item categories to the user such that the order is dependent upon the category scores.

27. The method of claim 26, wherein the step of determining a respective category score for each of the plurality of item categories comprises using one or more keywords of search query to look up associated score values.

28. The method of claim 26, wherein the step of determining a respective category score for each of the plurality of item categories comprises taking into consideration item popularity scores associated with items that are responsive to the search query.

29. The method of claim 26, wherein the category scores reflect current popularity levels of items that are responsive to the search query.

30. The method of claim 26, wherein the category scores are based in part on a profile of the user, such that the order in which the categories are presented is personalized for the user.

31. The method of claim 26, wherein the user activity data reflects user selections made on search results pages.

32. The method of claim 26, wherein the items are products that are available for purchase.

33. The method of claim 26, wherein automatically giving greater weight to user activity data associated with a most recent time period comprises using a sliding window method to select a set of user activity data to be taken into consideration.

34. The method of claim 26, wherein the automated analysis of the user activity data is repeated over time using a sliding time window such that the category scores, and associated category orderings, associated with a given search query change automatically over time to reflect current user preferences.

35. The method of claim 26, further comprising generating a search results page which lists the multiple item categories in said order, and which lists one or more responsive items in each of the multiple item categories.

36. A search results page generated according to the method of claim 35 represented in a computer memory.