Multi-variable product rank

• US 8,234,147 B2
• Filed: 05/15/2009
• Issued: 07/31/2012
• Est. Priority Date: 05/15/2009
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Olejniczak, Nicholas Jon, Moinuddin, Mohammed, Puetz, Joshua John, Burke, Keith McClelland
• Primary Examiner(s): Jasmin, Lynda
• Assistant Examiner(s): Miller, Alan S
• Application Number:
  US12/466,560
• Publication Number:
  US 20100293034A1
• Time in Patent Office: 1,173 Days
• Field of Search: 705/7.11, 705 729- 735, 705/40
• US Class Current: 705/7.29
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9538 Presentation of query results
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 10/101 Collaborative creation, e.g. joint development of products or services
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0246 Traffic
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products
• CPC Class Codes: G06Q 30/0601 Electronic shopping [e-shopping]
• CPC Class Codes: G06Q 30/0631 Item recommendations

Abstract

Methods, systems, and computer-readable media for ranking products using multiple data sources are provided. A computerized ranking system includes a ranking engine, loaders, and a presentation component. The ranking engine calculates a score for each product based on multiple counts logged by data sources. Loaders communicatively connected to the ranking engine provide the counts to the data sources. The presentation component generates a ranked product list for display on client devices in response to requests for a list of popular products.

Claims

1. One or more computer-readable media storing computer-usable instructions that cause one or more processors to perform a method that ranks products, the method comprising:

retrieving counts associated with each product from disparate data sources;

normalizing counts based on all products included in the database; and

assigning a rank to each product based on a score calculated from the normalized counts, wherein the score is calculated by summing the normalized counts in accordance with the following;

Score=α

P+β

C+δ

R+ζ

S, where P is a normalized number for page view for each product, C is a normalized number of clicks, R is a normalized revenue, S is a number of appearances in search results, α

is a weighting factor for P, β

is a weighting factor for C, δ

is a weighting factor for R, and ζ

;

is a weighting factor for S.

2. The media of claim 1, further comprising:

generating for display a list of products based on the assigned rank.

3. The media of claim 2, further comprising:

formatting the list of products as one of a HTML list, a XML list, or a RSS list.

4. A computer-implemented method to rank products, the method comprising:

receiving multiple counts for products from a plurality of data sources;

normalizing, by a processor of a computer, the counts for each product within each data source;

assigning, by a processor of a computer, a weight to each data source, wherein weight is used to calculate the score;

summing, by a processor of a computer, the normalized and weighted counts to calculate a score for each product, wherein the score is calculated using the following;

Score=α

P+β

C+δ

R+ζ

S, where P is a normalized number for page view for each product, C is a normalized number of clicks, R is a normalized revenue, S is a number of appearances in search results, α

is a weighting factor for P, β

is a weighting factor for C, δ

is a weighting factor for R, and ζ

;

is a weighting factor for S; and

generating, by a processor of a computer, a list based on the calculated score for each product.

5. The method of claim 4, wherein the counts are periodically received from the data sources.

6. The method of claim 4, wherein the product'"'"'s rank across discrete ranges of time is comparable based on the normalizations applied to counts.

7. The method of claim 4, wherein the normalized counts range from 0 to 1.

8. The method of claim 4, wherein the list includes categories and the ranks for products in each category are normalized.

9. A computerized ranking system, the ranking system comprising:

a computer processor coupled to a memory, wherein the computer processor is programmed to execute;

a ranking engine to calculate a score for each product stored in product databases, wherein the score is based on multiple counts logged by a plurality of data sources and is derived by summing normalized multiple counts in accordance with the following;

Score=α

P+β

C+δ

R+ζ

S, where P is a normalized number for page view for each product, C is a normalized number of clicks, R is a normalized revenue, S is a number of appearances in search results, α

is a weighting factor for P, β

is a weighting factor for C, δ

is a weighting factor for R, and ζ

;

is a weighting factor for S;

a plurality of loaders communicatively connected to the ranking engine, the loaders receive the counts for each product from the plurality of data sources; and

a presentation component to format a list of products, based on the scores calculated by the ranking engine, for display on client devices in response to requests for a list of popular products, wherein the display includes a graphical summary of score differences for the list of products over a period of time.

10. The computerized ranking system of claim 9, wherein the score is based on normalized counts logged by the plurality of data sources.

11. The computerized ranking system of claim 9, wherein the counts include number of page views, number of clicks, amount of revenue, number of entries in a search log.

12. The computerized ranking system of claim 9, wherein the counts include offline store transaction data.

13. The computerized ranking system of claim 9, wherein the counts include number of sales generates, number of seconds a user dwells on a product displayed on the computer.

14. The computerized ranking system of claim 9, wherein the loader is a device that accesses a data source to obtain the counts.

15. The computerized ranking system of claim 9, wherein the data sources are relational databases that store counts.

16. The computerized ranking system of claim 9, wherein the loaders periodically retrieves the counts from the multiple data source.

17. The computerized ranking system of claim 9, wherein each data source is associated with a specific loader.

18. The computerized ranking system of claim 9, wherein the ranking engine normalizes the scores based on categories selected by a user of client device to rank products based on score within the selected category.

19. The computerized ranking system of claim 9, wherein the presentation component is configured to output a ranked list in one of HTML format, RSS format, or XML format.