RECOMMENDER SYSTEM

• US 20100011020A1
• Filed: 07/11/2008
• Published: 01/14/2010
• Est. Priority Date: 07/11/2008
• Status: Abandoned Application

• Current Assignee: Google Technology Holdings LLC (Alphabet Inc.)
• Original Assignee: Motorola, Inc. (Motorola Solutions, Inc.)
• Inventors: Mercer, Kevin C., Bouzid, Makram, Lhuillier, Nicolas, Bonnefoy, David
• Application Number:
  US12/171,327
• Publication Number:
  US 20100011020A1
• Time in Patent Office: Days
• US Class Current: N/A
• CPC Class Codes: G06F 16/00 Information retrieval; Database structures therefor; File system structures therefor
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products

Abstract

A method for providing individualized recommendations to a user on a multi-user device is provided. During operation anonymous user preferences of similar program content will be grouped to form clusters of similar preferences. Context information for each cluster is determined and the clusters are grouped to form larger clusters. The grouping is based on the context information for each cluster. A current context is then determined and at least one larger cluster is found that has a similar context as the current context. The larger cluster is used to make a recommendation for the user.

Claims

1. A method for providing individualized recommendations to a user on a multi-user device, the method comprising the steps of:

grouping preferences of similar program content to form clusters of similar preferences;

determining context information for each cluster;

grouping clusters to form larger clusters, wherein the grouping is based on a similarity of context information of each cluster;

determining a current context;

choosing at least one larger cluster that has a similar context as the current context; and

using a larger cluster to make a recommendation for the current context.

2. The method of claim 1 wherein the context information comprises when each cluster'"'"'s content was viewed, where each cluster'"'"'s content was viewed, or on what device each cluster'"'"'s content was viewed.

3. The method of claim 1 wherein the step of grouping clusters to form larger clusters comprises forming larger clusters from clusters whose content was viewed at a similar time.

4. The method of claim 1 wherein the step of grouping clusters to form larger clusters comprises forming larger clusters from clusters whose content was viewed at a similar location.

5. The method of claim 1 wherein the step of grouping clusters to form larger clusters comprises forming larger clusters from clusters whose content was viewed on a similar device.

6. The method of claim 1 wherein the step of grouping preferences to form clusters comprises the step of using a clustering algorithm to form clusters.

7. The method of claim 1 wherein the step of using the larger cluster to make the recommendation comprises the steps of:

accessing an electronic programming guide;

choosing programming having a content similar to a content of the larger cluster; and

recommending a program that has the content similar to the content of the larger cluster.

8. The method of claim 1 wherein the recommendation comprises a TV program recommendation.

9. The method of claim 1 further comprising the step of:

collecting anonymous implicit or explicit user preferences, expressed as program ratings, comprising of a content part and a context part.

10. An apparatus comprising:

storage storing user preferences; and

a processor accessing the storage and grouping preferences of similar program content to form clusters of similar preferences, determining context information for each cluster and grouping clusters to form larger clusters, wherein the grouping is based on a similarity of context information of each cluster, the processor additionally accessing a context generator to determine a current context and choosing at least one larger cluster that has a similar context as the current context to make a recommendation.

11. The apparatus of claim 10 wherein the context information comprises when each cluster'"'"'s content was viewed, where each cluster'"'"'s content was viewed, or on what device each cluster'"'"'s content was viewed.

12. The apparatus of claim 10 wherein the larger clusters are formed from clusters whose content was viewed at a similar time.

13. The apparatus of claim 10 wherein the larger clusters are formed from clusters whose content was viewed at a similar location.

14. The apparatus of claim 10 wherein the larger clusters are formed from clusters whose content was viewed on a similar device.

15. The apparatus of claim 10 wherein a clustering algorithm is used to form clusters.

16. The apparatus of claim 10 wherein the recommendation comprises a TV program recommendation.