METHOD AND SYSTEM FOR SELECTION AND SCHEDULING OF CONTENT OUTLIERS

• US 20080104624A1
• Filed: 11/01/2006
• Published: 05/01/2008
• Est. Priority Date: 11/01/2006
• Status: Abandoned Application

• Current Assignee: Motorola, Inc. (Motorola Solutions, Inc.)
• Original Assignee: Motorola, Inc. (Motorola Solutions, Inc.)
• Inventors: Chaudhri, Rohit, Zurcher, Rodd B., Vesudevan, Venugopal, Narasimhan, Nitya
• Application Number:
  US11/555,517
• Publication Number:
  US 20080104624A1
• Time in Patent Office: Days
• US Class Current: 725/13
• CPC Class Codes: H04H 60/06 Arrangements for scheduling broadcast services or broadcast-related services
• CPC Class Codes: H04H 60/66 for using the result on distributors' side
• CPC Class Codes: H04N 21/44016 involving splicing one content stream with another content stream, e.g. for substituting a video clip
• 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 21/4622 Retrieving content or additional data from different sources, e.g. from a broadcast channel and the Internet web site content organization and management for information retrieval from the Internet G06F16/958; transmission by internet of broadcast information H04H60/82; stock exchange data over packet-switching network H04L12/1804; push services including data channel over packet-switching network H04L12/1859
• CPC Class Codes: H04N 21/4663 involving probabilistic networks, e.g. Bayesian networks
• CPC Class Codes: H04N 21/4667 Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections
• CPC Class Codes: H04N 21/812 involving advertisement data advertising per se G06Q30/02
• CPC Class Codes: H04N 21/84 Generation or processing of descriptive data, e.g. content descriptors systems specially adapted for using meta-information in broadcast systems H04H60/73

Abstract

A content recommendation system (100) to schedule a delivery of diverse content when a user is more receptive to the recommendation is provided. The system can include an outlier scheduling module (120) for scheduling an insertion of an outlier (224) in a recommended content based on a schedule model (125) and a trigger policy (127), an outlier selection module (140) for selecting the outlier from recommended content of an affinity model (145) based on a selection policy (147), and an outlier evaluation module (160) for monitoring a current user context and adjusting the selecting and the scheduling of the outlier in response to a user feedback of the outlier. The content recommendation system can expose the user to diverse content based on the user'"'"'s current consumption pattern and current context.

Claims

1. A content recommendation system, comprising:

an outlier scheduling module for scheduling an insertion of an outlier in a recommended content to provide content diversity at an appropriate time;

an outlier selection module coupled to the outlier scheduling module for selecting the outlier based on a selection policy; and

an outlier evaluation module coupled to the outlier selection module for monitoring a current user context and adjusting the selecting and the scheduling of the outlier in response to a user feedback of the outlier,

wherein an affinity model produces recommended content and the outlier selection module inserts the outlier in the recommended content to expose a user to alternative content based on the current user context.

2. The content recommendation system of claim 1, wherein the outlier scheduling module provides a contextual trigger to initiate outlier selection based on a schedule model and a trigger policy.

3. The content recommendation system of claim 2, wherein the contextual trigger is at least one of a random triggering, periodic triggering, context aware triggering, or resource aware triggering.

4. The content recommendation system of claim 1, wherein the outlier selection module selects outliers that are within a margin of tolerance, for recommendation.

5. The content recommendation system of claim 4, wherein the outlier selection module selects a size of the margin to dynamically expose the user to content that is within a degree of tolerance of the user'"'"'s current experience.

6. The content recommendation system of claim 5, wherein the outlier selection module changes the size of the margin based on the user feedback for tuning the scheduling and selection of outliers.

7. A method for diverse content recommendation, comprising:

determining an appropriate time to make an outlier recommendation in view of a current user consumption of content; and

triggering a selection and scheduling of an outlier in view of the appropriate time,

wherein the outlier is recommended at the appropriate times such that a user is introduced to diverse content a time that the user is more receptive to the diverse content.

8. The method of claim 7, wherein determining an appropriate time further comprises:

receiving a user request or a system policy decision for triggering the selection and scheduling of the outlier.

9. The method of claim 7, wherein triggering a selection and scheduling further comprises:

receiving recommended content from an affinity-driven channel;

scheduling an insertion time for an outlier in the recommended content to expose the user to alternate content at the appropriate time;

selecting an outlier in the recommended content in view of the current user consumption and according to a system-driven selection policy; and

monitoring a user acceptance of the outlier in the recommended content based on user feedback for adjusting the scheduling and selecting of the outlier.

10. The method of claim 9, wherein the step of scheduling an insertion time further comprises:

receiving a schedule and a trigger policy; and

determining a contextual trigger to initiate outlier selection based on the schedule and trigger policy.

11. The method of claim 10, the trigger policy is at least one of random triggering, periodic triggering, context-aware triggering, or resource-aware triggering.

12. The method of claim 9, further comprising:

selecting content that is available for scheduling and that is within a margin of the current user consumption;

adjusting a size of the margin based on the user acceptance; and

tuning a selection of the outlier based on the size of the margin, wherein the adjusting dynamically exposes the user to content that is within a degree of tolerance of the user'"'"'s current experience based on the current user consumption.

13. The method of claim 9, wherein the step of selecting an outlier further comprises:

evaluating a user affinity for the recommended content; and

identifying an outlier based on the user affinity.

14. The method of claim 9, wherein the step of selecting an outlier further comprises:

determining a margin size;

evaluating a selection policy; and

choosing outlier candidates in view of the margin size and the selection policy.

15. The method of claim 14, wherein the selection policy can include at least one of least-perturbation from normal, most-perturbation from normal, least-recently-heard, and not-currently owned.

16. The method of claim 9, wherein the step of monitoring a user acceptance further comprises:

receiving a user action in response to the outlier; and

reinforcing or invalidating the insertion of the outlier in view of the user action.

17. A media player for dynamically adapting to a user'"'"'s media experience needs, comprising:

an affinity model for producing recommended content;

a scheduling model for triggering an insertion of an outlier in the recommended content;

a media interface for playing the outlier and receiving user actions; and

a content recommendation system receiving the recommended content from the affinity model, a trigger policy from the scheduling model, and user feedback from the media interface for assessing current user consumption and context.

18. The media player of claim 17, wherein the content recommendation system includes:

an outlier scheduling module that receives input from the scheduling module and generates a trigger context to schedule the outlier in view of a trigger policy.

19. The media player of claim 18, wherein the content recommendation system further includes:

an outlier selection module coupled to the outlier scheduling module that receives the recommended content from the affinity driven model and determines an appropriate time to make an outlier recommendation in view of a selection policy and the trigger context.

20. The media player of claim 19, wherein the content recommendation system further includes:

an outlier evaluation module coupled to the outlier selection module and providing feedback to the affinity model for adjusting the selecting and the scheduling of the outlier in response to the user action provided by the media interface.