Grouping of item data using seed expansion
First Claim
1. A system comprising:
- a computer-readable memory storing executable instructions; and
one or more processors in communication with the computer-readable memory, the one or more processors programmed by the executable instructions to at least;
obtain data regarding a plurality of item groups, wherein data regarding a first item group of the plurality of item groups comprises a first keyword with which the first item group is associated;
determine, using a keyword-to-keyword map, a second keyword associated with the first keyword;
determine, using a keyword-to-item map, a first item associated with the second keyword;
add the first item to the first item group based at least partly on the first item being associated with the second keyword;
obtain an item connection graph comprising a first node representing the first item, a second node representing a second item, and a connection between the first node and the second node, wherein the connection indicates a similarity between the first item and the second item;
select the first node as a seed node based at least in part on the first item being in the first item group;
assign a first score to the first node, the first score based on the first node being a seed node;
determine a second score for the second node using the first score and a decay factor, wherein the first score is used to determine the second score based at least partly on the connection between the first node and the second node; and
add the second item to the first item group based at least partly on the second score.
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Accused Products
Abstract
Features are provided for the analysis of collections of data and automatic grouping of data having certain similarities. A collection of data regarding user interactions with item-specific content can be analyzed. The analysis can be used to identify groups of items that are of interest to groups of similar users and/or to identify groups of users with demonstrated interests in groups of similar items. Data may be analyzed in a “bottom-up” manner in which correlations within the data are discovered in an iterative manner, or in a “top-down” manner in which desired top-level groups are specified at the beginning of the process. A bottom-up process may also be distributed among multiple devices or processors to more efficiently discover groups when using large collections of data.
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Citations
20 Claims
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1. A system comprising:
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a computer-readable memory storing executable instructions; and one or more processors in communication with the computer-readable memory, the one or more processors programmed by the executable instructions to at least; obtain data regarding a plurality of item groups, wherein data regarding a first item group of the plurality of item groups comprises a first keyword with which the first item group is associated; determine, using a keyword-to-keyword map, a second keyword associated with the first keyword; determine, using a keyword-to-item map, a first item associated with the second keyword; add the first item to the first item group based at least partly on the first item being associated with the second keyword; obtain an item connection graph comprising a first node representing the first item, a second node representing a second item, and a connection between the first node and the second node, wherein the connection indicates a similarity between the first item and the second item; select the first node as a seed node based at least in part on the first item being in the first item group; assign a first score to the first node, the first score based on the first node being a seed node; determine a second score for the second node using the first score and a decay factor, wherein the first score is used to determine the second score based at least partly on the connection between the first node and the second node; and add the second item to the first item group based at least partly on the second score. - View Dependent Claims (2, 3, 4)
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5. A computer-implemented method comprising:
as performed by a computing system comprising a processor configured to execute specific instructions, obtaining an item graph comprising a first node, a second node, and a connection between the first node and the second node, wherein the connection indicates a similarity between a first item represented by the first node and a second item represented by the second node; selecting the first node as a seed node based at least partly on data regarding an association between the first item and a keyword, wherein the keyword is associated with an item group to which items are to be added; determining a first score for the first node based at least partly on the first node being the seed node; determining a second score for the second node using the first score, wherein determining the second score comprises applying a weighting factor to the first score, and wherein the first score is used to determine the second score based at least partly on the connection between the first node and the second node; and adding the second item to the item group based at least partly on the second score, wherein the first item is also in the item group. - View Dependent Claims (6, 7, 8, 9, 10, 11, 12)
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13. A non-transitory computer storage medium storing executable instructions that, when executed by one or more processors of a computing system, cause the one or more processors to perform a process comprising:
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obtaining an item graph comprising a first node, a second node, and a connection between the first node and the second node, wherein the connection indicates a similarity between a first item represented by the first node and a second item represented by the second node; selecting the first node as a seed node based at least partly on data regarding an association between the first item and a keyword, wherein the keyword is associated with an item group to which items are to be added; determining a first score for the first node based at least partly on the first node being the seed node; determining a second score for the second node using the first score, wherein determining the second score comprises applying a weighting factor to the first score, and wherein the first score is used to determine the second score based at least partly on the connection between the first node and the second node; and adding the second item to the item group based at least partly on the second score, wherein the first item is also in the item group. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20)
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Specification