Method and system for providing customized recommendations to users

• US 20070143128A1
• Filed: 12/20/2005
• Published: 06/21/2007
• Est. Priority Date: 12/20/2005
• Status: Abandoned Application

• Current Assignee: Trusted Opinion, Inc.
• Original Assignee: Trusted Opinion, Inc.
• Inventors: Akselrod, Ilya, Drujina, Vadim, Tokarev, Maxim, Shahar (Boris), Smirin
• Application Number:
  US11/314,519
• Publication Number:
  US 20070143128A1
• Time in Patent Office: Days
• US Class Current: 705/347
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products

Abstract

In one embodiment, a method includes transforming ratings provided by users for an object of interest into one or more correlated recommendations based on the level of trust of a recommendation recipient towards the users, and presenting the correlated recommendations to the recommendation recipient.

Claims

1. A computerized method comprising:

transforming ratings provided by users for an object of interest into one or more correlated recommendations based, at least in part, on a level of trust of a recommendation recipient towards the users; and

presenting the one or more correlated recommendations to the recommendation recipient.

2. The method of claim 1 wherein the ratings provided by the users include ratings provided by any of the users for one or more items associated with the object of interest.

3. The method of claim 2 wherein transforming ratings provided by the users for the object of interest into one or more correlated recommendations comprises:

for every item associated with the object of interest,

identifying a plurality of coefficients for each user that provided a rating for said item,

calculating, based on the plurality of coefficients, a weight factor for each user that provided a rating for said item,

applying weight factors to ratings of corresponding users for said item, and

calculating a weighted average of resulting weighted ratings for said item.

4. The method of claim 3 wherein the plurality of coefficients comprises coefficients selected from the group consisting of a trust level coefficient, an expertise coefficient, a timing coefficient, a similarity coefficient, and a ratings number coefficient.

5. The method of claim 4 wherein the trust level coefficient is specified by the recommendation recipient or determined automatically.

6. The method of claim 3 wherein transforming ratings provided by the users for the object into one or more correlated recommendations further comprises:

adjusting the weighted average of resulting weighted ratings using a total number of ratings provided for a relevant item.

7. The method of claim 1 further comprising:

identifying, within a plurality of users, users that are likely to be trusted by the recommendation recipient; and

retrieving the ratings provided by the identified users from a database.

8. The method of claim 7 wherein the users that are likely to be trusted by the recommendation recipient are identified based on relationships among the plurality of users within a social network.

9. The method of claim 8 wherein the social network is built based on input of the plurality of users or automatically.

10. The method of claim 7 wherein the users that are likely to be trusted by the recommendation recipient are identified based on virtual relationships among the plurality of users.

11. The method of claim 10 further comprising:

determining the virtual relationships based on factors selected from the group consisting of communications among the plurality of users, profiles of the plurality of users, and behavioral patterns of the plurality of users.

12. The method of claim 11 wherein the communications are selected from the group consisting of email communications, instant messaging (IM) communications, chat communications, voice over IP (VoIP) communications, and mobile phone communications.

13. The method of claim 1 further comprising:

collecting ratings from a plurality users; and

storing the ratings in a database.

14. The method of claim 13 wherein the ratings are collected via at least one of a request to provide a minimum number of recommendations during a user registration, a request to provide at least one recommendation in response to a user receipt of a predefined number of recommendations, and a request to rate a specific item.

15. The method of claim 13 further comprising:

providing a wizard to motivate the plurality of users to provide ratings.

16. The method of claim 1 further comprising:

receiving a request concerning the object of interest.

17. An apparatus comprising:

a weighted rating calculator to transform ratings provided by users for an object of interest into one or more correlated recommendations based, at least in part, on a level of trust of a recommendation recipient towards the users; and

a recommendation presenter to present the one or more correlated recommendations to the recommendation recipient.

18. The apparatus of claim 17 wherein the weighted rating calculator is to transform ratings provided by the users for the object of interest into one or more correlated recommendations by:

for every item associated with the object of interest,

identifying a plurality of coefficients for each user that provided a rating for said item,

calculating, based on the plurality of coefficients, a weight factor for each user that provided a rating for said item,

applying weight factors to ratings of corresponding identified users for said item, and

calculating a weighted average of resulting weighted ratings for said item.

19. The apparatus of claim 18 wherein the plurality of coefficients comprises coefficients selected from the group consisting of a trust level coefficient, an expertise coefficient, a timing coefficient, a similarity coefficient, and a ratings number coefficient.

20. The apparatus of claim 17 further comprising:

a trusted user identifier to identify, within a plurality of users, users that are likely to be trusted by the recommendation recipient.

21. The apparatus of claim 20 wherein the trusted user identifier identifies the users that are likely to be trusted by the recommendation recipient based on virtual relationships among the plurality of users or relationships among the plurality of users within a social network.

22. The apparatus of claim 17 further comprising:

a request receiver to receive a request concerning an object of interest.

23. A machine-readable medium containing instructions which, when executed by a processing system, cause the processing system to perform a method, the method comprising:

receiving a request of a recommendation recipient for a recommendation concerning an object;

transforming ratings provided by users for the object into one or more correlated recommendations based, at least in part, on a level of trust of a recommendation recipient towards the users; and

presenting the one or more correlated recommendations to the recommendation recipient.