RECOMMENDING SIMILAR CONTENT IDENTIFIED WITH A NEURAL NETWORK
First Claim
1. One or more computer-readable media having computer-executable instructions embodied thereon for performing a method of finding similar visual objects within a plurality of visual objects, the method comprising:
- storing the plurality of visual objects in a data store, wherein each visual object within the plurality of visual objects includes an identifier and visual content;
storing a neural network that includes a plurality of nodes, wherein each node in the neural network is associated with an individual visual object in the plurality of visual objects, and wherein each node includes at least one similarity coefficient that indicates a degree of similarity between the node and another node;
receiving a first selection of a first visual object from a user, wherein the first visual object is one of the plurality of visual objects;
determining that a second visual object is similar to the first visual object because a similarity coefficient between a first node and a second node indicates that the second visual object is similar to the first visual object, wherein the first node corresponds to the first visual object and the second node corresponds to the second visual object;
communicating the second visual object for presentation to the user.
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Accused Products
Abstract
Methods, systems and computer-readable media for finding similarities between visual objects by evaluating user interactions with a collection of visual objects are provided. Using a neural network, human interactions with a collection of visual objects are evaluated to ascertain relationships or connections between visual objects. The relationship between visual objects indicates that the visual objects are similar. Once relationships between visual objects are identified, a user may select one or more visual objects and receive suggested visual objects that are similar to the one or more visual objects selected by the user.
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Citations
20 Claims
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1. One or more computer-readable media having computer-executable instructions embodied thereon for performing a method of finding similar visual objects within a plurality of visual objects, the method comprising:
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storing the plurality of visual objects in a data store, wherein each visual object within the plurality of visual objects includes an identifier and visual content; storing a neural network that includes a plurality of nodes, wherein each node in the neural network is associated with an individual visual object in the plurality of visual objects, and wherein each node includes at least one similarity coefficient that indicates a degree of similarity between the node and another node; receiving a first selection of a first visual object from a user, wherein the first visual object is one of the plurality of visual objects; determining that a second visual object is similar to the first visual object because a similarity coefficient between a first node and a second node indicates that the second visual object is similar to the first visual object, wherein the first node corresponds to the first visual object and the second node corresponds to the second visual object; communicating the second visual object for presentation to the user. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A computerized system, including one or more computer-readable media, containing a two-layer neural network for finding similar visual objects within a plurality of visual objects, the system comprising:
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the two-layer neural network containing a plurality of nodes that are linked by similarity coefficients, wherein; (1) each node is associated with a visual object in a data store, and (2) a sum of the similarity coefficients associated with the each node must equal a first number or zero, wherein if the sum of the similarity coefficients is zero for a single node then the single node is not related to any other nodes in the two-layer neural network; and a similarity search component for; (1) receiving a selection of one or more selected visual objects that are each associated with a corresponding node, (2) identifying one or more similar visual objects by evaluating the similarity coefficients associated with the each node associated with each of the one or more selected visual objects, and (3) causing the one or more similar visual objects to be presented to a user. - View Dependent Claims (11, 12, 13, 14, 15)
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16. A method for training a neural network to determine that a first visual object is similar to a second visual object, the method comprising:
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receiving a first selection of the first visual object returned in response to a query; receiving a second selection of the second visual object returned in response to the query; creating a training set including a first identifier for the first visual object as an input and a second identifier for the second visual object as an output, wherein the training set is used to adjust a plurality of similarity coefficients associated with nodes in a neural network so that when the training set input is submitted to the neural network the neural network produces the training set output in response; inputting the training set into the neural network; and updating a similarity coefficient from a first node in the neural network that corresponds to the first visual object to a second node in the neural network that corresponds to the second visual object. - View Dependent Claims (17, 18, 19, 20)
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Specification