Statistical personalized recommendation system

• US 20060259344A1
• Filed: 07/18/2006
• Published: 11/16/2006
• Est. Priority Date: 08/19/2002
• Status: Abandoned Application

• Current Assignee: Choicestream Incorporated
• Original Assignee: Choicestream Limited (Choicestream Incorporated)
• Inventors: Patel, Jayendu, Strickman, Michael
• Application Number:
  US11/488,416
• Publication Number:
  US 20060259344A1
• Time in Patent Office: Days
• US Class Current: 705/9
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities
• 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 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

A method for recommending items in a domain to users, either individually or in groups, makes user of users'"'"' characteristics, their carefully elicited preferences, and a history of their ratings of the items are maintained in a database. Users are assigned to cohorts that are constructed such that significant between-cohort differences emerge in the distribution of preferences. Cohort-specific parameters and their precisions are computed using the database, which enable calculation of a risk-adjusted rating for any of the items by a typical non-specific user belonging to the cohort. Personalized modifications of the cohort parameters for individual users are computed using the individual-specific history of ratings and stated preferences. These personalized parameters enable calculation of a individual-specific risk-adjusted rating of any of the items relevant to the user. The method is also applicable to recommending items suitable to groups of joint users such a group of friends or a family. A related method can be used to discover users who share similar preferences. Similar users to a given user are identified based on the closeness of the statistically computed personal-preference parameters.

Claims

1. A method for identifying similar users comprising:

maintaining a history of ratings of the items by users in a group of users;

computing parameters using the history of ratings, said parameters being associated with the group of users and enabling computation of a predicted rating of any of the items by an unspecified user in the group;

computing personalized statistical parameters for each of one or more individual users in the group using the parameters associated with the group and the history of ratings of the items by that user, said personalized parameters enabling computation of a predicted rating of any of the items by that user;

identifying similar users to a first user using the computed personalized statistical parameters for the users.

2. The method of claim 1 wherein identifying the similar users includes computing predicted ratings on a set of items for the first user and a set of potentially similar users, and selecting the similar users from the set according to the predicted ratings.

3. The method of claim 1 wherein identifying the similar users includes identifying a social group.

4. The method of claim 3 wherein the social group includes members of a computerized chat room.

5. Software stored on a computer readable media comprising instructions for causing a computer system to perform functions comprising:

maintaining a history of ratings of the items by users in a group of users;

computing parameters using the history of ratings, said parameters being associated with the group of users and enabling computations of a predicted rating of any of the items by an unspecified user in a group;

computing personalized statistical parameters for each of one or more individual users in the group using the parameters associated with the group and the history of ratings of the items by that user, said personalized parameters enabling computation of a predicted rating of any of the items of that user;

identifying similar users to a first user using the computed personalized statistical parameters for the users.