Three-way media recommendation method and system

• US 7,937,725 B1
• Filed: 07/27/2000
• Issued: 05/03/2011
• Est. Priority Date: 07/27/2000
• Status: Expired due to Fees

• Current Assignee: Sisvel S.p.A. (Fineur International S.A.)
• Original Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Inventors: Gutta, Srinivas, Lee, Kwok Pun, Schaffer, J. David
• Primary Examiner(s): HUYNH, AN SON PHI
• Application Number:
  US09/627,139
• Time in Patent Office: 3,932 Days
• Field of Search: 725/46, 725/47, 725/26, 705/10, 705/14, 705/26
• US Class Current: 725/46
• CPC Class Codes: H04N 21/44224 Monitoring of user activity on external systems, e.g. Internet browsing
• 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 21/454 Content or additional data filtering, e.g. blocking advertisements
• CPC Class Codes: H04N 21/466 Learning process for intelligent management, e.g. learning user preferences for recommending movies services using the results of monitoring in broadcast systems H04H60/61
• 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 21/4668 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 21/4756 for rating content, e.g. scoring a recommended movie
• CPC Class Codes: H04N 21/4821 using a grid, e.g. sorted out by channel and broadcast time
• 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

An electronic programming guide (EPG) system employing a preference engine and processing system that combines explicit rule profile, history profile, and feedback profile data to generate new predictions. Television shows are presumed to be indexed by many features. These features are extracted and counted for TV shows watched (implicit profile), and for TV shows rated by the viewer (feedback profile). These profiles are straightforward to combine with suitably greater weight being given to the feedback information. In addition, explicit profiles can make recommendations that stand alone or may be used to modify recommendations arising from either of the two sources. The modifications may take the form of additive or multiplicative changes to the existing recommendations or some other suitable mathematical form.

Claims

1. An automated recommendation system, comprising

a processor connected to receive resource data defining available resources and at least two sets of profile data, each defining a user'"'"'s preferences with respect to the resources;

each of the sets of profile data being derived from a different class of interaction of the user with a first portion of the resource data and usable to predict a given resource'"'"'s desirability based on each of the sets;

the processor being adapted to;

generate at least two sets of predictions based on one or a combination of the sets of profile data, and

combine the predictions by weight-averaging corresponding ones from each of the at least two sets of predictions.

2. A system as in claim 1, wherein

the processor is further adapted to:

generate a weighted sum of corresponding records from each of the sets of profile data to generate a single combined set of profile data, and

generate at least one of the sets of predictions from the single combined set.

3. A system as in claim 2, wherein

the processor is connected to control a delivery of resources corresponding to the resource data and responsively to the predictions.

4. A system as in claim 1, wherein

the processor is connected to control a delivery of resources corresponding to the resource data and responsively to the predictions.

5. A system as in claim 1, wherein

the at least two profile data sets include

a feedback data set derived from ratings provided by the user with respect to a particular resource in the resource data.

6. A system as in claim 1, wherein

the at least two profile data sets include

an implicit data set derived from machine-observation of a user'"'"'s resource use history, whereby the implicit data reflects the user'"'"'s selections of resources to use.

7. A system as in claim 1, wherein

at least one set of the at least two profile data sets comprises input vectors, and

the input vectors each include feature-value pairs.

8. A system as in claim 1, wherein

at least one set of the at least two profile data sets comprises input vectors, and

the input vectors include feature-value pairs and a rating value.

9. A method of recommending resources, comprising:

generating by a processing device, at least two sets of profile data based on expressed preferences of a user with respect to the resources, each being usable to predict a given resource'"'"'s desirability based on each of the sets;

generating by a processing device, at least two sets of predictions based on one or a combination of the sets of profile data; and

combining, by a processing device, the predictions by weight-averaging corresponding ones from each of the at least two sets of predictions.

10. A method as in claim 9, further comprising:

generating a weighted sum of corresponding records from each of the sets of profile data to generate a single combined set of profile data; and

generating at least one of the sets of predictions from the single combined set.

11. A method as in claim 10, further comprising

controlling a delivery of resources corresponding to the resource data responsively to the predictions.

12. A method as in claim 9, further comprising

controlling a delivery of resources corresponding to the resource data responsively to the predictions.

13. A method as in claim 9, wherein

generating the at least two sets of profile data includes

generating a feedback data set by accepting ratings from the user with respect to a particular resource in the resource data.

14. A method as in claim 9, wherein

generating the at least two sets of profile data includes

generating an implicit data set by observing the user'"'"'s resource use history, whereby the implicit data reflects the user'"'"'s selections of resources to use.

15. A method as in claim 9, wherein

at least one set of the at least two sets of profile data comprises input vectors, and

the input vectors each include feature-value pairs.

16. A method as in claim 9, wherein

at least one set of the at least two sets of profile data comprises input vectors, and

generating the at least two sets of profile data includes generating feature-value pairs and a rating value.

17. A method as in claim 9, wherein:

the sets of profile data includes;

a set of explicit profile data indicating express indications by a user of preferred classes of programming rather than indications by the user of particular resources that are preferred;

a feedback data set derived from ratings provided by the user with respect to a particular resource in the resource data; and

an implicit data set derived from machine-observation of a user'"'"'s resource use history, whereby the implicit data reflects the user'"'"'s selection.

18. An automated recommendation system, comprising:

a processor connected to receive resource data defining available resources and sets of profile data, each defining a users preferences with respect to the resources;

the sets of profile data including;

a set of explicit profile data indicating express indications by a user of preferred classes of programming rather than indications by the user of particular resources that are preferred;

feedback data set derived from ratings provided by the user with respect to a particular resource in the resource data; and

an implicit data set derived from machine-observation of a users resource use history, whereby the implicit data reflects the user'"'"'s selection;

the processor being adapted to generate at least two sets of predictions based on one or a combination of the sets of profile data, each of the predictions including a confidence level;

the processor being further adapted to combine the predictions by weight-averaging corresponding ones from each of the at least two sets of predictions.

19. A system as in claim 18, wherein

the processor is further adapted to adjust weights of the weight averaging responsively to a difference between the corresponding ones.

20. A system as in claim 18, wherein

the processor is further adapted to selectively override the weight averaging responsively to a difference between the corresponding ones.

21. A method of automatically recommending resources, comprising:

receiving resource data defining available resources and sets of profile data, each defining user preferences with respect to the resources;

the sets of profile data including;

a set of explicit profile data indicating express indications by a user of preferred classes of programming rather than indications by the user of particular resources that are preferred;

a feedback data set derived from ratings provided by the user with respect to a particular resource in the resource data; and

an implicit data set derived from machine-observation of a user'"'"'s resource use history, whereby the implicit data reflects the user'"'"'s selection;

generating at least two sets of predictions based on one or a combination of the sets of profile data, each of the predictions including a confidence level; and

combining the predictions by weight-averaging corresponding ones from each of the at least two sets of predictions to produce a combined set.

22. A method in claim 21, wherein

combining the predictions includes

adjusting weights of the weight averaging responsively to a difference between the corresponding ones.

23. A method as in claim 21, wherein

combining the predictions includes

selectively overriding the weight averaging responsively to a difference between the corresponding ones such that a prediction of a one of the sets of predictions is included in the combined set and a prediction of the other of the sets of predictions is excluded.

24. A method of combining profile data, comprising:

generating first profile data by receiving through a user interface user preferences in the form of expressed generalized preferences corresponding classes of resources;

generating second profile data by receiving user preferences in the form of rating data corresponding to specific resources; and

applying the first and second profile data to respective prediction engines to produce first and second prediction results and combining the first and second results.

25. A method as in claim 24, further including

combining the first and second profile data,

wherein

combining the first and second profiles includes weight averaging corresponding ones of the profile data.

26. A method as in claim 24, wherein

combining respective results includes selectively weight averaging corresponding ones of the predictions.