HEBBIAN LEARNING-BASED RECOMMENDATIONS FOR SOCIAL NETWORKS

• US 20170188101A1
• Filed: 12/28/2015
• Published: 06/29/2017
• Est. Priority Date: 12/28/2015
• Status: Active Grant

• Current Assignee: Verizon Patent and Licensing Incorporated (Verizon Communications Inc.)
• Original Assignee: Verizon Patent and Licensing Incorporated (Verizon Communications Inc.)
• Inventors: Srinivasaraghavan, Haripriya
• Granted Patent: US 10,362,137 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 11/34 Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
• CPC Class Codes: G06F 11/3438 monitoring of user actions tracking the activity of the user H04L67/535
• CPC Class Codes: H04L 43/04 Processing captured monitoring data, e.g. for logfile generation
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• CPC Class Codes: H04L 67/30 Profiles
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04L 67/53 using third party service providers
• CPC Class Codes: H04N 21/252 Processing of multiple end-users' preferences to derive collaborative data
• CPC Class Codes: H04N 21/25891 being end-user preferences retrieval of video data in a video database based on user preferences G06F16/739; arrangements for recognizing users' preferences H04H60/46; 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 21/4532 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 21/4662 characterized by learning algorithms
• CPC Class Codes: H04N 21/4668 for recommending content, e.g. movies
• CPC Class Codes: H04N 21/4788 communicating with other users, e.g. chatting arrangements for providing for computer conferences, e.g. chat rooms, to substation in data switching networks H04L12/1813; distributed application using peer-to-peer [P2P] networks H04L67/104
• 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 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 network device applies Hebbian-based learning to provide content recommendations in content-based social networks. The method includes obtaining customer activity data for a content-based social network; modeling the customer activity data as nodes and edges within the content-based social network, the nodes representing users and the edges representing connections between the users; assigning initial weights to the edges, that correspond to a connection strength, based on user-designated of relationships between the nodes; adjusting the initial weights in response to temporally correlated activity between the nodes from the customer activity data, to provide adjusted weights; identifying a content recommendation for a particular node based on an activity to access content by another node and one or more of the adjusted weights; storing a customer profile including the content recommendations associated with a node; and providing the content recommendation to a user device associated with the customer profile.

Claims

1. A method comprising:

obtaining, by a network device, customer activity data for a content-based social network;

modeling, by the network device, the customer activity data as nodes and edges within the content-based social network, the nodes representing users and the edges representing connections between the users;

assigning, by the network device, initial weights to the edges, that correspond to a connection strength, based on user-designated of relationships between the nodes;

adjusting, by the network device, the initial weights in response to temporally correlated activity between the nodes from the customer activity data, to provide adjusted weights;

identifying, by the network device, a content recommendation for a particular node based on an activity to access content by another node and one or more of the adjusted weights;

storing, by the network device, a customer profile including the content recommendations associated with a node; and

providing, by the network device, the content recommendation to a user device associated with the customer profile.

2. The method of claim 1, wherein the customer activity data includes selection of a content item from a catalog of available content items or selection of a recommended content item.

3. The method of claim 2, wherein the content item includes one of a television content, video-on-demand content, peer-to-peer content, or advertising content.

4. The method of claim 1, wherein assigning the initial weights further comprises:

assigning high initial weights to direct connections between nodes; and

assigning nominal initial weights to indirect connections between nodes.

5. The method of claim 1, the adjusting further comprising:

increasing one or more of the initial weights by an amount that is proportional to a difference between an expected outcome of the temporally correlated activity and an actual outcome of the temporally correlated activity, or

decreasing one or more of the initial weights by an amount that is proportional to the difference between the expected outcome of the temporally correlated activity and the actual outcome of the temporally correlated activity.

6. The method of claim 5, wherein the expected outcome is one of:

selecting particular content, which was previously selected by another node, within a correlation time window; and

failing to select the particular content, which was previously selected by the other node, within the correlation time window.

7. The method of claim 1, wherein the customer profile includes one or more of:

a group association of the customer based on the connection strengths,

an influencer designation, or

an influenced designation.

8. The method of claim 7, wherein providing the content recommendation includes one or more of:

identifying a viewership trend for particular content and providing the content recommendation based on the trend; and

sending the content recommendation to all nodes within a same group as a node that previously selected particular content.

9. The method of claim 1, wherein the customer profile includes a connection strength threshold for notifications, and

wherein providing the content recommendation includes sending the content recommendation to all nodes having weights of corresponding edges that exceed the connection strength threshold.

10. A device comprising:

one or more memories to store instructions; and

one or more processing systems to execute the instructions that configure the one or more processing systems to;

obtain customer activity data for a content-based social network;

model the customer activity data as nodes and edges within the content-based social network, the nodes representing users and the edges representing connections between the users;

assign initial weights to the edges, that correspond to a connection strength, based on user-designated of relationships between the nodes;

adjust the initial weights in response to temporally correlated activity between the nodes from the customer activity data, to provide adjusted weights;

identify a content recommendation for a particular node based on an activity to access content by another node and one or more of the adjusted weights;

store, a customer profile including the content recommendations associated with a node; and

provide the content recommendation to a user device associated with the customer profile.

11. The device of claim 10, wherein the customer activity data includes selection of a content item from a catalog of available content items or selection of a recommended content item.

12. The device of claim 10, wherein the content item includes advertising content.

13. The device of claim 10, wherein when assigning the initial weights, the one or more processing systems execute the instructions that further configure the one or more processing systems to:

assign high initial weights to direct connections between nodes; and

assign nominal initial weights to indirect connections between nodes.

14. The device of claim 13, wherein when adjusting the initial weights, the one or more processing systems execute the instructions that further configure the one or more processing systems to:

increase one or more of the initial weights by an amount that is of a first proportion to a difference between an expected outcome of the temporally correlated activity and an actual outcome of the temporally correlated activity, when the expected outcome is selecting particular content, which was previously selected by another node, within a correlation time window.

15. The device of claim 14, wherein when updating the initial weights, the one or more processing systems execute the instructions that further configure the one or more processing systems to:

decrease one or more of the initial weights by an amount that is of a second proportion to a difference between another expected outcome of the temporally correlated activity and the actual outcome of the temporally correlated activity, when the other expected outcome is failing to select the particular content, which was previously selected by the other node, within the correlation time window.

16. The device of claim 10, wherein the customer profile includes one or more of:

a group association of the customer based on the connection strengths,

an influencer designation, or

an influenced designation.

17. The device of claim 10, wherein when providing the content recommendation, the one or more processing systems execute the instructions that further configure the one or more processing systems to:

identify a group, based on the connection strengths, that includes a node that previously selected particular content, and

send the content recommendation to all nodes within the group.

18. A non-tangible computer-readable medium storing instructions for:

obtaining customer activity data for a content-based social network;

modeling the customer activity data as nodes and edges within the content-based social network, the nodes representing users and the edges representing connections between the users;

assigning initial weights to the edges, that correspond to a connection strength, based on user-designated of relationships between the nodes;

adjusting the initial weights in response to temporally correlated activity between the nodes from the customer activity data, to provide adjusted weights;

identifying a content recommendation for a particular node based on an activity to access content by another node and one or more of the adjusted weights;

storing a customer profile including the content recommendations associated with a node; and

providing the content recommendation to a user device associated with the customer profile.

19. The non-tangible computer-readable medium of claim 18, wherein the instructions for assigning weights further include instructions for:

assigning high initial weights to direct connections between nodes; and

assigning nominal initial weights to indirect connections between nodes.

20. The non-tangible computer-readable medium of claim 19, wherein the instructions for adjusting the initial weights further include instructions for:

increasing one or more of the initial weights by an amount that is proportional to a difference between an expected outcome of the temporally correlated activity and an actual outcome of the temporally correlated activity, or

decreasing one or more of the initial weights by an amount that is proportional to the difference between the expected outcome of the temporally correlated activity and the actual outcome of the temporally correlated activity,

wherein the expected outcome of the temporally correlated activity includes a user selection of television content, video-on-demand content, peer-to-peer content, or advertising content that corresponds to the content recommendation.