System and method for influencing recommender system

• US 8,103,540 B2
• Filed: 06/07/2004
• Issued: 01/24/2012
• Est. Priority Date: 06/05/2003
• Status: Active Grant

• Current Assignee: Xylon LLC (Intellectual Ventures LLC)
• Original Assignee: Hayley Logistics LLC (Intellectual Ventures LLC)
• Inventors: Gross, John N.
• Primary Examiner(s): Boyce, Andre
• Assistant Examiner(s): Anderson, Folashade
• Application Number:
  US10/863,642
• Publication Number:
  US 20040267604A1
• Time in Patent Office: 2,787 Days
• Field of Search: 705/7.33, 705/10, 705/1, 707/770, 707/758
• US Class Current: 705/7.33
• 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 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0203 Market surveys; Market polls
• CPC Class Codes: G06Q 30/0204 Market segmentation

Abstract

A system and method for implementing/influencing a recommender system which provides recommendations to users based on characteristics of certain trendsetters within a member population. The trendsetters are determined by studying historical adoption behavior of a group within the member population, or by reference to known indicia.

Claims

1. A method for suggesting items of interest to a community of online subscribers, comprising:

determining, with a network computing device, a first set of items that have been adopted by more than half the subscribers within the community of online subscribers;

computing an adoption rate for each online subscriber, wherein the adoption rate comprises;

aggregating each online subscriber'"'"'s adoption score for at least one item of the first set of items to produce an aggregated adoption score, and

dividing the aggregated adoption score by a number of items from the first set of items actually adopted by the online subscriber;

identifying respective subscribers within the community of online subscribers by ranking each online subscriber'"'"'s adoption rate;

classifying a predefined percentage of the respective subscribers as trendsetters;

measuring, with a network computing device, a second adoption rate by the trendsetters for a particular item of a second set of items; and

making recommendations by the recommender system for the first particular item of the second set of items based on a value of the second adoption rate.

2. The method of claim 1 wherein the second set of items include at least one of human readable content, products and services.

3. The method of claim 1, wherein a trendsetter is selected by identifying an item from the first set of items that achieves a greater than 50% adoption rate within the community of online subscribers, and then identifying the first N persons who adopted such item.

4. The method of claim 1, wherein said trendsetters are at least one of users purchasers, and renters of the first or second set of items.

5. The method of claim 1, wherein the recommender system uses a collaborative filtering algorithm, and said trendsetters are weighted more heavily than other subscribers within the community of online subscribers.

6. The method of claim 1, further comprising:

identifying trend rejecters for said first particular item; and

modifying a recommender system recommendation by reducing contributions by said trend rejecters for said particular item.

7. The method of claim 1, further comprising:

identifying a second particular item of the second set of items rejected by said trendsetters; and

modifying a recommendation for said second particular item based on a rejection rate exhibited by said trendsetters.

8. The method of claim 1, wherein the adoption score is a measure of how a person adopted an item.

9. A method for suggesting items of interest to a community of online subscribers comprising:

determining, with a network computing network device, a first set of items that have been adopted by more than half the subscribers within the community of online subscribers;

computing an adoption rate for each online subscriber, wherein the adoption rate comprises;

aggregating each online subscriber'"'"'s adoption score for at least one item of the first set of items to produce an aggregated adoption score, and

dividing the aggregated adoption score by a number of items from the first set of items actually adopted by the online subscriber;

identifying respective subscribers within the community of online subscribers by ranking each online subscriber'"'"'s adoption rate;

classifying a predefined percentage of respective subscribers of the ranked subscriber adoption ratings as trendsetters;

measuring, with a network computing device, a trendsetter rating for a first particular item of a second set of items provided by said trendsetters;

modifying a user rating for said first particular item for other subscribers based on said trendsetter rating and;

using said trendsetter rating to generate recommendations to a user requesting a suggestion from an electronic recommender system.

10. The method of claim 9, further comprising:

identifying a second particular item of the second set of items rejected by said trendsetters; and

generating a recommendation for said second particular item based on a rejection rate exhibited by said trendsetters.

11. The method of claim 9, wherein said trendsetters are associated with web pages on the Internet.

12. The method of claim 9, wherein the adoption score is a measure of how a person adopted an item.

13. A system for providing recommendations of items of interest to a community of online subscribers, comprising:

a network computing device executing software module which is configured to;

determine a first set of items that have been adopted by more than half of subscribers within the community of online subscribers;

compute an adoption rate for each online subscriber, wherein the adoption rate comprises;

aggregating each online subscriber'"'"'s adoption score for at least one item of the first set of items to produce an aggregated adoption score, and

dividing the aggregated adoption score by a number of items from the first set of items actually adopted by the online subscriber;

identify respective subscribers within the community of online subscribers by ranking each online subscriber'"'"'s adoption rate;

classify a predefined percentage of the respective subscribers of the ranked subscriber adoption ratings as trendsetters;

measure a second adoption rate by the trendsetters for a first particular item of a second set of items; and

provide recommendations for said particular item of the second set of items based on a value of second adoption rate.

14. The system of claim 13, wherein said trendsetters are at least one of users, purchasers, and renters of the first set of items.

15. The system of claim 13 wherein said software module includes a collaborative filtering algorithm in which said trendsetters are weighted more heavily than other subscribers within the community of online subscribers.

16. The system of claim 13, wherein the software module is further configured to identify trend rejecters for said first particular item, and provide a recommendation by reducing contributions by said trend rejecters for said particular item.

17. The system of claim 13, further comprising:

the network computing device executing a second software module for adjusting advertising presented with said recommendations based on adoption rate.

18. The system of claim 13, wherein the trendsetters are associated with individual web pages.

19. The method of claim 13, wherein the adoption score is a measure of how a person adopted all item.