FINDING THE SPACE SPANNED BY USER PROFILES FROM BINARY FEEDBACK

• US 20160110761A1
• Filed: 10/15/2014
• Published: 04/21/2016
• Est. Priority Date: 11/01/2013
• Status: Abandoned Application

• Current Assignee: Ioannidis, Efstratios, Montanari, Andrea, Yuekai Sun
• Original Assignee: Ioannidis, Efstratios, Montanari, Andrea, Yuekai Sun
• Inventors: Ioannidis, Efstratios, Montanari, Andrea, Sun, Yuekai
• Application Number:
  US14/514,844
• Publication Number:
  US 20160110761A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 30/0255 based on user history

Abstract

Finding the space spanned by user profiles of consumed items for making recommendations commences by first estimating a mean and covariance for a set of labeled items associated with a profile. Thereafter, a vector is identified that belongs to a convex cone spanned by the user profiles based on the estimated mean and covariance, the labels and items. The labels are mirrored in a negative space defined by the identified vector. The weighted covariance matrix is computed based on the mirrored labels; and eigenvalues and eigenvectors are computed of the weighted covariance matrix. A first set of eigenvalues share a value and wherein a remainder of the eigenvalues correspond to eigenvectors that span the profile.

Claims

1. A method for providing recommendations of items based on user behavior, comprising:

tracking user which items offered to users were purchased,

storing user purchasing behavior in a user profile, and

determining a span of user profiles; and

making item recommendations based of the span of user profiles.

2. A method for finding the space spanned by user profiles, comprising:

estimating a mean and covariance for a set of labeled items associated with a profile;

identifying a vector that belongs to a convex cone spanned by the user profiles based on the estimated mean and covariance, the labels and items;

mirroring the labels in a negative space defined by the identified vector;

computing a weighted covariance matrix based on the mirrored labels; and

computing eigenvalues and eigenvectors of the weighted covariance matrix, wherein a first set of eigenvalues share a value and wherein a remainder of the eigenvalues correspond to eigenvectors that span the profile.

3. The method of claim 2, wherein a number of the remainder eigenvalues corresponds to a number of users associated with the profile.

4. The method of claim 2, wherein the labeled items are labeled according to a binary label.

5. The method of claim 4, wherein mirroring comprises flipping a binary value of labels in the negative space.

6. The method of claim 2, further comprising one of clustering or predicting using the eigenvectors that span the profile.

7. The method of claim 2, further comprising rotating the eigenvectors that span the profile by multiplying them with a covariance matrix.

8. A system for finding the space spanned by user profiles, comprising:

a storage device configured to store a set of labeled items associated with a user profile; and

a processor configured to estimate a mean and covariance for the set of labeled items, to identify a vector that belongs to a convex cone spanned by the user profiles based on the estimated mean and covariance, the labels and items, to mirror the labels in a negative space defined by the identified vector, to compute a weighted covariance matrix based on the mirrored labels, and to compute eigenvalues and eigenvectors of the weighted covariance matrix, wherein a first set of eigenvalues share a value and wherein a remainder of the eigenvalues correspond to eigenvectors that span the profile.

9. The system of claim 8, wherein a number of the remainder eigenvalues corresponds to a number of users associated with the profile.

10. The system of claim 8, wherein the labeled items are labeled according to a binary label.

11. The system of claim 10, wherein the processor is configured to mirror by flipping a binary value of labels in the negative space.

12. The system of claim 8, wherein the processor is further configured to cluster or predict using the eigenvectors that span the profile.

13. The system of claim 8, wherein the processor is further configured to rotate the eigenvectors that span the profile by multiplying them with a covariance matrix.

14. A non-transitory computer readable storage medium comprising a computer readable program for finding the space spanned by user profiles, wherein the computer readable program when executed on a computer causes the computer to perform the steps of:

estimating a mean and covariance for a set of labeled items associated with a profile;

identifying a vector that belongs to a convex cone spanned by the user profiles based on the estimated mean and covariance, the labels and items;

mirroring the labels in a negative space defined by the identified vector;

computing a weighted covariance matrix based on the mirrored labels; and

computing eigenvalues and eigenvectors of the weighted covariance matrix, wherein a first set of eigenvalues share a value and wherein a remainder of the eigenvalues correspond to eigenvectors that span the profile.