HEBBIAN LEARNING-BASED RECOMMENDATIONS FOR SOCIAL NETWORKS
First Claim
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 designated relationships between the nodes;
adjusting, by the network device, the initial weights in response to temporally correlated selections of a content item by two or more of the nodes, as indicated in the customer activity data, to provide adjusted weights, wherein the adjusting further comprises;
increasing one or more of the initial weights by an amount based on a difference between an expected outcome of the temporally correlated selections of the content item and an actual outcome of the temporally correlated selections of the content item, wherein the expected outcome includes selection of particular content by a particular node of the two or more nodes, which was previously selected by another node of the two or more nodes, within a time window;
identifying, by the network device, a content recommendation for the particular node of the two or more nodes based on an activity to access content by the other node and one or more of the adjusted weights; and
providing, by the network device, the content recommendation to a user device associated with the particular node.
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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.
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20 Claims
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1. A method comprising:
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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 designated relationships between the nodes; adjusting, by the network device, the initial weights in response to temporally correlated selections of a content item by two or more of the nodes, as indicated in the customer activity data, to provide adjusted weights, wherein the adjusting further comprises; increasing one or more of the initial weights by an amount based on a difference between an expected outcome of the temporally correlated selections of the content item and an actual outcome of the temporally correlated selections of the content item, wherein the expected outcome includes selection of particular content by a particular node of the two or more nodes, which was previously selected by another node of the two or more nodes, within a time window; identifying, by the network device, a content recommendation for the particular node of the two or more nodes based on an activity to access content by the other node and one or more of the adjusted weights; and providing, by the network device, the content recommendation to a user device associated with the particular node. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A device comprising:
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one or more memories to store instructions; and a processor to execute the instructions 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 designated relationships between the nodes; adjust the initial weights in response to temporally correlated selections of a content item by two or more of the nodes, as indicated in the customer activity data, to provide adjusted weights, wherein the adjusting further comprises; increasing one or more of the initial weights by an amount base on a difference between an expected outcome of the temporally correlated selections of the content item and an actual outcome of the temporally correlated selections of the content item, wherein the expected outcome includes selection by a particular node of particular content, which was previously selected by another node of the two or more nodes, within a time window; identify a content recommendation for the particular node of the two or more nodes based on an activity to access content by the other node and one or more of the adjusted weights; and provide the content recommendation to a user device associated with the particular node. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17)
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18. A non-transitory computer-readable storage medium storing instructions executable by a processor of a device, which when executed cause the device to:
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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 designated relationships between the nodes; adjust the initial weights in response to temporally correlated selections of a content item by two or more of the nodes, as indicated in the customer activity data, to provide adjusted weights, wherein the adjusting further comprises; increasing one or more of the initial weights by an amount based on a difference between an expected outcome of the temporally correlated selections of the content item and an actual outcome of the temporally correlated selections of the content item, wherein the expected outcome includes selection by a particular node of particular content, which was previously selected by another node of the two or more nodes, within a time window; identify a content recommendation for the particular node of the two or more nodes based on an activity to access content by the other node and one or more of the adjusted weights; and provide the content recommendation to a user device associated with the particular node. - View Dependent Claims (19, 20)
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Specification