Method and apparatus for using cluster compactness as a measure for generation of additional clusters for stereotyping programs
First Claim
1. A method for partitioning a plurality of items into groups of similar items, the plurality of items corresponding to a selection history by at least one third party, the method comprising the steps of:
- partitioning the selection history into k clusters, k having an initial value of at least two;
identifying at least one mean item for each of the k clusters;
assigning each of the plurality of items to one of the k clusters based on a distance metric;
determining a measure of cluster compactness for at least one of the k clusters; and
incrementing the value of k if a predetermined criteria is not met by the measure of cluster compactness and then repeating the steps.
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Accused Products
Abstract
A method and apparatus are disclosed for recommending items of interest to a user, such as television program recommendations, before a viewing history or purchase history of the user is available. A third party viewing or purchase history is processed to generate stereotype profiles that reflect the typical patterns of items selected by representative viewers. A user can select the most relevant stereotype(s) from the generated stereotype profiles and thereby initialize his or her profile with the items that are closest to his or her own interests. A clustering routine partitions the third party viewing or purchase history (the data set) into clusters using a k-means clustering algorithm, such that points (e.g., television programs) in one cluster are closer to the mean of that cluster than any other cluster. The value of k is incremented in accordance with a measure of cluster compactness.
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Citations
19 Claims
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1. A method for partitioning a plurality of items into groups of similar items, the plurality of items corresponding to a selection history by at least one third party, the method comprising the steps of:
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partitioning the selection history into k clusters, k having an initial value of at least two;
identifying at least one mean item for each of the k clusters;
assigning each of the plurality of items to one of the k clusters based on a distance metric;
determining a measure of cluster compactness for at least one of the k clusters; and
incrementing the value of k if a predetermined criteria is not met by the measure of cluster compactness and then repeating the steps. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A system for partitioning a plurality of items into groups of similar items, the plurality of items corresponding to a selection history by at least one third party, the system comprising:
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a memory for storing computer readable code; and
a processor operatively coupled to said memory, said processor configured to;
partition the selection history into k clusters, k having an initial value of at least two;
identify at least one mean item for each of the k clusters;
assign each of the plurality of items to one of the k clusters based on a distance metric;
determine a measure of cluster compactness for at least one of the k clusters; and
increment the value of k if a predetermined criteria is not met by the measure of cluster compactness. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18)
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19. An article of manufacture for partitioning a plurality of items into groups of similar items, said plurality of items corresponding to a selection history by at least one third party, comprising:
a computer readable medium having computer readable code means embodied thereon, said computer readable program code means comprising;
a step to partition said third party selection history into k clusters;
a step to identify at least one mean item for each of said k clusters;
a step to assign each of said plurality of items to one of said clusters based on a distance metric;
a step to determine a measure of cluster compactness for at least one of the k clusters; and
a step to increment the value of k if a predetermined criteria is not met by the measure of cluster compactness.
Specification