Apparatus and Method for Content Recommendation

• US 20090222430A1
• Filed: 02/28/2008
• Published: 09/03/2009
• Est. Priority Date: 02/28/2008
• Status: Abandoned Application

• Current Assignee: Motorola Mobility, Inc. (Lenovo Group Ltd.)
• Original Assignee: Motorola, Inc. (Motorola Solutions, Inc.)
• Inventors: Hobson, Paola M., Baranov, Sergey N.
• Application Number:
  US12/039,376
• Publication Number:
  US 20090222430A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/437 Administration of user profiles, e.g. generation, initialisation, adaptation, distribution

Abstract

A content recommender executes a method wherein a set of attributes are provided for a plurality of content items. A number of recommendation parameters are defined as a function of attribute values for a subset of attributes and for each content item, recommendation values are determined based on the definitions. A multi-dimensional clustering is applied to the recommendation values for the content items to generate a plurality of content clusters. Each dimension of the clustering corresponds to a recommendation parameter. The content recommender then selects a set of content clusters from the content clusters and a recommendation set of content items is generated by selecting at least one content item from each selected content cluster. The invention may allow improved recommendations to be generated and may in particular allow recommendations to be generated which reflect a number of different and possibly conflicting considerations.

Claims

1. A method of content recommendation comprising:

providing a set of attributes for each content item of a plurality of content items;

determining a recommendation value for a first number of recommendation parameters for each of the plurality of content items, each recommendation parameter being defined as a function of attribute values for a subset of attributes of the set of attributes;

generating a plurality of content clusters by a multi-dimensional clustering of the plurality of content items in response to the recommendation values, each dimension corresponding to a recommendation parameter;

selecting a first set of content clusters from the plurality of content clusters; and

generating a first recommendation set of content items by selecting at least one content item from each content cluster of the first set of content clusters.

2. The method of claim 1 wherein the subsets of attributes for different recommendation parameters are disjoint.

3. The method of claim 1 wherein the recommendation parameters are orthogonal.

4. The method of claim 1 wherein at least one recommendation parameter is indicative of a user preference for the content item.

5. The method of claim 1 wherein a first subset of attributes for a first content item comprises characterising data for the first content item, and a recommendation value for the first content item is determined in response to the characterising data and a user preference profile for the user.

6. The method of claim 1 wherein at least one recommendation parameter is indicative of a cost measure associated with consuming the content item.

7. The method of claim 1 further comprising determining at least one recommendation value in response to a cost measure received from a remote source, the cost measure indicating a cost associated with consuming the content item.

8. The method of claim 7 wherein the remote source is a content provider server, and the method further comprises receiving at least one content item of the plurality of content items from the content provider server.

9. The method of claim 1 wherein at least one recommendation parameter is dependent on user preference data and not dependent on a cost measure associated with consuming the content item, and at least one other recommendation parameter is dependent on the cost measure and not dependent on the user preference data.

10. The method of claim 1 further comprising selecting the first set of content clusters in response to a multi-dimensional distance between origin and each cluster of the plurality of clusters.

11. The method of claim 10 wherein the first set of content clusters is selected as a predetermined number of clusters having a largest multi-dimensional distance to origin.

12. The method of claim 1 further comprising selecting the first set of content clusters in response to a multi-dimensional distance between clusters of the plurality of clusters.

13. The method of claim 12 wherein the first set of content clusters is selected as a predetermined number of clusters having a largest multi-dimensional distance between them.

14. The method of claim 1 wherein selecting the first set of content clusters comprises:

generating a constraint satisfaction problem having a set of constraints relating to cluster locations; and

selecting content clusters for the first set of content clusters by solving the constraint satisfaction problem.

15. The method of claim 14 wherein the set of constraints comprises constraints related to at least one of:

a. a distance between selected clusters;

b. a distance from origin to selected clusters; and

c. constraints of individual recommendation values for selected clusters.

16. The method of claim 14 further comprising relaxing the set of constraints in response to a determination that a solution to the constraint satisfaction problem does not meet a criterion.

17. The method of claim 1 further comprising generating a second recommendation set by selecting at least one different content item from at least one content cluster of the first set of content clusters in response to a user input.

18. The method of claim 1 further comprising generating in response to a user input a second recommendation set by selecting a second set of content clusters from the plurality of content clusters in response to a selection criterion which is different than a selection criterion used to select the first set of content clusters.

19. The method of claim 1 wherein selecting the set of content clusters comprises selecting a predetermined number of content clusters for the set of content clusters.

20. A content item recommendation apparatus comprising:

a unit for receiving a set of attributes for each content item of a plurality of content items;

a unit for determining a recommendation value for a first number of recommendation parameters for each of the plurality of content items, each recommendation parameter being defined as a function of attribute values for a subset of attributes of the set of attributes;

a unit for generating a plurality of content clusters by a multi-dimensional clustering of the plurality of content items in response to the recommendation values, each dimension corresponding to a recommendation parameter;

a unit for selecting a first set of content clusters from the plurality of content clusters; and

a unit for generating a first recommendation set of content items by selecting at least one content item from each content cluster of the first set of content clusters.