Method and apparatus for generating a stereotypical profile for recommending items of interest using item-based clustering

• US 20030097196A1
• Filed: 11/13/2001
• Published: 05/22/2003
• Est. Priority Date: 11/13/2001
• Status: Abandoned Application

• Current Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Original Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Inventors: Gutta, Srinivas, Kurapati, Kaushal
• Application Number:
  US10/014,192
• Publication Number:
  US 20030097196A1
• Time in Patent Office: Days
• US Class Current: 700/100
• CPC Class Codes: H04N 21/252 Processing of multiple end-users' preferences to derive collaborative data
• CPC Class Codes: H04N 21/4532 involving end-user characteristics, e.g. viewer profile, preferences monitoring of user activities for profile generation for accessing a video database G06F16/739; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18
• CPC Class Codes: H04N 21/4662 characterized by learning algorithms
• CPC Class Codes: H04N 21/4668 for recommending content, e.g. movies
• CPC Class Codes: H04N 21/4755 for defining user preferences, e.g. favourite actors or genre retrieval personalisation and generation of user profiles for the retrieval of video data G06F16/739; user profiles in network data switching protocols H04L67/306
• CPC Class Codes: H04N 21/4826 using recommendation lists, e.g. of programs or channels sorted out according to their score
• CPC Class Codes: H04N 7/163 by receiver means only

Abstract

A method and apparatus are disclosed for recommending items of interest to a user, such as television program recommendations, before a viewing history or purchase history of the user is available. A third party viewing or purchase history is processed to generate stereotype profiles that reflect the typical patterns of items selected by representative viewers. A user can select the most relevant stereotype(s) from the generated stereotype profiles and thereby initialize his or her profile with the items that are closest to his or her own interests. A clustering routine partitions the third party viewing or purchase history (the data set) into clusters using a k-means clustering algorithm, such that points (e.g., television programs) in one cluster are closer to the mean of that cluster than any other cluster. A mean computation routine computes the symbolic mean of a cluster. For an item -based mean computation, the distance computation between two items is performed on the item level and the resultant cluster mean is made up of the feature values of the selected mean item. Thus, the one or more items that exhibit the minimum variance are selected as the mean of that cluster.

Claims

1. A method for identifying one or more mean items for a plurality of items, J, each of said items having at least one symbolic attribute, each of said symbolic attributes having at least one possible value, said method comprising the steps of:

computing a variance for each of said items; and

selecting at least one item that minimizes said variance as the mean symbolic value.

2. The method of claim 1, wherein said symbolic values for said at least one selected item comprise said mean of said plurality of items.

3. The method of claim 1, further comprising the step of assigning a label to said plurality of items using at least one symbolic value from said selected item.

4. The method of claim 1, wherein said plurality of items are a cluster including similar items.

5. The method of claim 1, wherein said items are programs.

6. The method of claim 1, wherein said items are content.

7. The method of claim 1, wherein said items are products.

8. The method of claim 1, wherein said step of computing a variance is performed as follows:

Var(J)=j=Σ

<sub>iε

J</sub>(x_i−

x_μ

)²

where J is a cluster of items from the same class, x_i is an item, i, and x_μ

is the item(s) in said plurality of items, J, such that it minimizes said Var (J).

9. A method for characterizing a plurality of items, J, each of said items having at least one symbolic attribute, each of said symbolic attributes having at least one possible value, said method comprising the steps of:

computing a variance for each of said items; and

characterizing said plurality of items, J, with at least one mean item by selecting at least one item that minimizes said variance as the mean symbolic value.

10. The method of claim 9, wherein said symbolic values for said at least one selected item comprise said mean of said plurality of items.

11. The method of claim 9, further comprising the step of assigning a label to said plurality of items using at least one symbolic value from said at least one mean item.

12. The method of claim 9, wherein said plurality of items are a cluster including similar items.

13. The method of claim 9, wherein said step of computing a variance is performed as follows:

Var(J)=Σ

<sub>iε

J</sub>(x_i−

x_μ

)²

where J is a cluster of items from the same class, x_i is an item, i, and x_μ

is the item(s) in said plurality of items, J, such that it minimizes said Var (J).

14. A system for identifying one or more mean items for a plurality of items, J, each of said items having at least one symbolic attribute, each of said symbolic attributes having at least one possible value, said system comprising:

a memory for storing computer readable code; and

a processor operatively coupled to said memory, said processor configured to;

compute a variance for each of said items; and

select at least one item that minimizes said variance as the mean symbolic value.

15. The system of claim 14, wherein said symbolic values for said at least one selected item comprise said mean of said plurality of items.

16. The system of claim 14, wherein said processor is further configured to assign a label to said plurality of items using at least one symbolic value from said selected item.

17. The system of claim 14, wherein said plurality of items are a cluster including similar items.

18. The system of claim 14, wherein said step of computing a variance is performed as follows:

Var(J)=Σ

<sub>iε

J</sub>(x_i−

x_μ

)²

where J is a cluster of items from the same class, x_i is an item, i, and x_μ

is the item(s) in said plurality of items, J, such that it minimizes said Var (J).

19. An article of manufacture for identifying one or more mean items for a plurality of items, J, each of said items having at least one symbolic attribute, each of said symbolic attributes having at least one possible value, comprising:

a computer readable medium having computer readable code means embodied thereon, said computer readable program code means comprising;

a step to compute a variance for each of said items; and

a step to select at least one item that minimizes said variance as the mean symbolic value.

20. A system for identifying one or more mean items for a plurality of items, J, each of said items having at least one symbolic attribute, each of said symbolic attributes having at least one possible value, said system comprising:

means for computing a variance for each of said items; and

means for selecting at least one item that minimizes said variance as the mean symbolic value.