Method and system for ranking search results based on category demand normalized using impressions

• US 8,996,495 B2
• Filed: 02/15/2011
• Issued: 03/31/2015
• Est. Priority Date: 02/15/2011
• Status: Active Grant

• Current Assignee: eBay Inc.
• Original Assignee: eBay Inc.
• Inventors: Rehman, Muhammad Faisal
• Primary Examiner(s): Thai, Hanh
• Application Number:
  US13/027,991
• Publication Number:
  US 20120209831A1
• Time in Patent Office: 1,505 Days
• Field of Search: 707/706, 707/708, 707/723, 707/727, 707/748, 707/758, 707/771, 707/710, 707/736
• US Class Current: 707/706
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/954 Navigation, e.g. using categorised browsing

Abstract

Described herein are methods and systems for promoting item listings that satisfy a query based on the item listings being assigned to certain categories that have, based on historical click data, exhibited high demand characteristics for the query. Consistent with some embodiments, a certain number of leaf-level categories are identified based on demand data for those categories, and the item listings assigned to those categories are promoted through a normalized weighting factor derived in part based on the click probability score associated with the category, clicks per impression rate, and weighted clicks per impression by ranking rate.

Claims

1. A computer-implemented method comprising:

processing a query, by a hardware processor of a machine, to identify a set of item listings, each item listing associated with an item or service being offered and assigned to a leaf-level category;

identifying a leaf-level category for each item listing satisfying the query;

obtaining for the query a click probability score for each leaf-level category to which an item listing satisfying the query has been assigned;

identifying up to a predetermined number of leaf-level categories from all leaf-level categories from the set of item listings identified with the query with click probability scores exceeding a same threshold score for all leaf-level categories;

for each of the identified leaf-level categories, calculating a category boost score for use in determining the order in which the item listings are to be presented in a search results page;

normalizing the category boost score for one or more identified leaf-level categories; and

presenting a search results page with the item listings ordered based in part on the normalized category boost score for the leaf-level category to which each item listing is assigned.

2. The computer-implemented method of claim 1, wherein normalizing further comprises:

calculating a constant boost score for the one or more identified leaf-level categories.

3. The computer-implemented method of claim 1, wherein normalizing further comprises:

determining a respective number of clicks per impression for each leaf-level category; and

calculating the normalized category boost score of a leaf-level category based on the respective number of clicks per impression.

4. The computer-implemented method of claim 1, wherein normalizing further comprises:

determining a weighted impression by rank for each leaf-level category;

determining a respective weighted number of clicks per impression by rank for each leaf-level category; and

calculating the normalized category boost score of each leaf-level category based on the respective weighted number of clicks per impression by rank.

5. The computer-implemented method of claim 1, wherein the threshold score is derived as a percentage of the click probability score of the leaf-level category with a highest click probability score.

6. The computer-implemented method of claim 1, wherein the threshold score is derived by dividing the click probability score of the leaf-level category with a highest click probability score by one less than the predetermined number.

7. The computer-implemented method of claim 1, wherein the category boost score for each identified leaf-level category is derived based in part on the click probability score of each identified leaf-level category.

8. The computer-implemented method of claim 1, wherein the item listings are ordered based on a ranking score derived with an algorithm utilizing the category boost score as a factor.

9. The computer-implemented method of claim 1, wherein the click probability score for each category represents a probability, for a particular query, that an item listing assigned to the category will be selected from a search results page, the click probability score for each category derived based on analysis of historical click data.

10. The computer-implemented method of claim 1, wherein obtaining a click probability score for each leaf-level category to which an item listing satisfying the query has been assigned includes dividing a number of clicks for a particular leaf-level category by the total number of clicks for all leaf-level categories to which an item listing satisfying the query has been assigned.

11. A system for an item listing presentation management, the system comprising:

at least one processor comprising;

a listing identifier module configured to process a query to identify a set of item listings, each item listing associated with an item or service being offered and assigned to a leaf-level category, and to identify a leaf-level category for each item listing satisfying the query;

a probability score module configured to obtain for the query a click probability score for each leaf-level category to which an item listing satisfying the query has been assigned, and identifying up to a predetermined number of leaf-level categories from all leaf-level categories from the set of item listings identified with the query with click probability scores exceeding a same threshold score for all leaf-level categories;

a category boost module configured to calculate a category boost score, for each of the identified leaf-level categories, for use in determining the order in which the item listings are to be presented in a search results page;

a normalizing module, implemented with the least one processor, configured to normalize the category boost score for one or more identified leaf-level categories; and

a listing generator module configured to present a search results page with the item listings ordered based in part on the normalized category boost score for the leaf-level category to which each item listing is assigned.

12. The system of claim 11, wherein the normalizing module comprises:

a constant boost module configured to calculate a constant boost score for the one or more identified leaf-level categories.

13. The system of claim 11, wherein the normalizing module comprises:

a click through rate module configured to determine a respective number of clicks per impression for each leaf-level category, and to calculate the normalized category boost score of a leaf-level category based on the respective number of clicks per impression.

14. The system of claim 11, wherein the normalizing module comprises:

a weight click through rate by rank module configured to determine a weighted impression by rank for each leaf-level category, to determine a respective weighted number of clicks per impression by rank for each leaf-level category, and to calculate the normalized category boost score of each leaf-level category based on the respective weighted number of clicks per impression by rank.

15. The system of claim 11, wherein the item listing presentation management module is to derive the threshold score as a percentage of the click probability score of the leaf-level category with a highest click probability score.

16. The system of claim 11, wherein the item listing presentation management module is to derive the threshold score by dividing the click probability score of the leaf-level category with a highest click probability score by one less than the predetermined number.

17. The system of claim 11, wherein the item listing presentation management module is to derive the category boost score for each identified leaf-level category based in part on the click probability score of each identified leaf-level category.

18. The system of claim 11, wherein the item listing presentation management module is to ordered the item listings based on a ranking score derived with an algorithm utilizing the category boost score as a factor.

19. The system of claim 11, wherein the click probability score for each category represents a probability, for a particular query, that an item listing assigned to the category will be selected from a search results page, the click probability score for each category derived based on analysis of historical click data.

20. The system of claim 11, wherein obtaining a click probability score for each leaf-level category to which an item listing satisfying the query has been assigned includes dividing a number of clicks for a particular leaf-level category by the total number of clicks for all leaf-level categories to which an item listing satisfying the query has been assigned.

21. A non-transitory computer-readable storage medium storing a set of instructions that, when executed by a processor, cause the processor to perform operations, comprising:

processing a query to identify a set of item listings, each item listing associated with an item or service being offered and assigned to a leaf-level category;

identifying a leaf-level category for each item listing satisfying the query;

obtaining for the query a click probability score for each leaf-level category to which an item listing satisfying the query has been assigned;

identifying up to a predetermined number of leaf-level categories from all leaf-level categories from the set of item listings identified with the query with click probability scores exceeding a same threshold score for all leaf-level categories;

for each of the identified leaf-level categories, calculating a category boost score for use in determining the order in which the item listings are to be presented in a search results page;

normalizing the category boost score for one or more identified leaf-level categories; and

presenting a search results page with the item listings ordered based in part on the normalized category boost score for the leaf-level category to which each item listing is assigned.