AUTOMATIC DETERMINATION OF GENRE-SPECIFIC RELEVANCE OF RECOMMENDATIONS IN A SOCIAL NETWORK

• US 20140136621A1
• Filed: 07/13/2012
• Published: 05/15/2014
• Est. Priority Date: 07/22/2011
• Status: Active Grant

• Current Assignee: Funke TV Guide GmbH
• Original Assignee: Axel Springer Digital TV Guide GmbH
• Inventors: Korst, Jan, Barbieri, Mauro, Pronk, Serverius Petrus Paulus
• Granted Patent: US 10,404,824 B2
• Time in Patent Office: Days
• US Class Current: 709/204
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 67/306 User profiles

Abstract

The present invention relates to an operating method of operating a recommender system, a filtering apparatus (260) for a recommender system (200), a recommender system and a corresponding computer program. An idea of the invention is to automatically learn for a user A in a social network, which recommendations of contacts of user A, who are also members of the social network, are relevant with respect to a genre into which user A is interested in. A learning algorithm is used to interpret feedback from user A in response to receiving recommendations from his/her contacts. Thereby, for each combination of a contact and a genre, a relevance-taste index can be determined. The determined relevance-taste index is subjected to a filter. Only such recommendations are provided to user A, whose associated relevance-taste indices fulfil a filtering criterion. Thereby, the amount of irrelevant recommendations submitted to user A can be significantly reduced.

Claims

1. A method of operating a personal recommender system arranged for being coupled to a computer implemented social network, the method comprising the steps of:

a) detecting that a recommendation of a contact-genre tuple (CU1,g) has been filed in a user account of a user A of the social network, wherein the recommendation of the contact-genre tuple (CU1,g) relates to a content item x of a specific genre (g=g(x)) and has been initiated through a contact account of a contact CU1 of user A,

b) monitoring a recommendation related reaction behaviour of user A in response to receiving the recommendation,

c) repeating steps a) to b) for a plurality of recommendations of the same contact-genre tuple (CU1,g) and logging a plurality of monitored reaction behaviours for determining a user A related relevance-taste index r=r((CU1,g)) associated to the contact-genre tuple (CU1,g) in dependence of the plurality of monitored reaction behaviours, and

d) filtering a current recommendation of the same contact-genre tuple (CU1,g) by filing the current recommendation in the user account only, if a filtering criterion is fulfilled by the relevance-taste index of the current recommendation and by blocking the current recommendation, if the filtering criterion is not fulfilled.

2. The method of claim 1, comprising the additional steps of:

buffering current recommendations that have been sent to the user account but have been blocked and therefore not been provided to user A, and

filing at least one of the buffered recommendations in the user account, if the at least one recommendation fulfils an adjusted filtering criterion.

3. The method of claim 2, comprising the additional steps of:

grouping blocked recommendations being related to a common content item y,

determining an accumulated relevance-taste index by summing relevance-taste indices associated to each of the grouped recommendations, and

filing the grouped recommendations as a single combined recommendation in the user account, if the accumulated relevance-taste index fulfils the filtering criterion.

4. The method of claim 3, additionally comprising the steps of:

identifying senders F(A) of the grouped recommendations, wherein each of the senders F(A) has an associated contact account that is linked to the user account of user A,

for each of the identified senders F(A), determining a like-degree λ

(B,y) for the common content item y, the like-degree λ

(B,y) indicating the respective sender'"'"'s B interest or disinterest in the common content item y,

calculating a normalized accumulated relevance-taste index in dependence of the determined like-degrees and the accumulated relevance-taste index, wherein the normalized accumulated relevance-taste index corresponds to an assumed like-degree λ

(A,y) indicating user A'"'"'s interest or disinterest in the common content item y, and

filing a recommendation for the common content item y in the user account, if the normalized accumulated relevance-taste index fulfils the filtering criterion.

5. The method of claim 4, wherein the normalized accumulated relevance-taste index λ

(A, y) is calculated according to the formula

λ

(A, y)=Σ

B ε

F(A)r(B, g(y))·

λ

(B, y)/Σ

B ε

F(A)|r(B, g(y))|,

wherein Σ

B ε

F(A) r(B, g(y)) is the accumulated relevance-taste index.

6. The method of claim 5, additionally comprising the step of:

presenting filtered recommendations to user A through a display of a user interface coupled to the personal recommender system by employing a colour encoding for encoding a respective assumed like-degree of each of the presented recommendations.

7. The method of claim 1, wherein the step of determining the relevance-taste index includes applying a learning algorithm, such as applying a neural network, a Naive Bayes classifier, or a support vector machine.

8. The method of claim 7, wherein applying the learning algorithm includes taking into account a warning relating to a content item, which has been submitted through the contact account, the warning indicating a contact'"'"'s dislike with respect to the content item.

9. A medium configured to store a computer program for operating a recommender system, the computer program comprising program code means for causing the recommender system to carry out the steps of claim 1, when the computer program is run on a computer controlling the recommender system.

15. The method of claim 1, comprising the additional steps of:

grouping blocked recommendations being related to a common content item y,

determining an accumulated relevance-taste index by summing relevance-taste indices associated to each of the grouped recommendations, and

filing the grouped recommendations as a single combined recommendation in the user account, if the accumulated relevance-taste index fulfils the filtering criterion.

16. The method of claim 4, additionally comprising the step of:

presenting filtered recommendations to user A through a display of a user interface coupled to the personal recommender system by employing a colour encoding for encoding a respective assumed like-degree of each of the presented recommendations.

10. A filter apparatus arranged for being coupled to a recommender system for a user A of a computer implemented social network, the filter apparatus comprising:

a detector configured to detect that a recommendation of a contact-genre tuple (CU1,g) has been filed in a user account of user A of the social network, wherein the recommendation of the contact-genre tuple (CU1,g) relates to a content item x of a specific genre (g=g(x)) and has been initiated through a contact account of a contact CU1 of user A,

a monitoring unit configured to monitor a recommendation related reaction behaviour of user A in response to receiving the recommendation,

a data logger configured to log a plurality of monitored reaction behaviours being related to a plurality of recommendations of the same contact-genre tuple (CU1,g),

an analyser configured determine a user A related relevance-taste index r=r((CU1,g)) associated to the contact-genre tuple in dependence of the plurality of monitored reaction behaviours, and

a recommendation filter configured to filter a current recommendation of the same contact-genre tuple (CU1,g) by filing the current recommendation in the user account only, if a filtering criterion is fulfilled by the relevance-taste index of the current recommendation and by blocking the current recommendation, if the filtering criterion is not fulfilled.

11. A recommender system for a user A of a computer implemented social network, the recommender system comprising a filtering apparatus according to claim 10.

12. The recommender system of claim 11, additionally comprising a user interface for presenting a filtered recommendation to user A.

13. The recommender system of claim 12, additionally comprising a personal recommender engine coupled to the user interface and configured to provide an engine based recommendation.

14. The recommender system of claim 11 additionally comprising a memory configured to store blocked recommendations.