SITUATION RECOGNITION FOR RECOMMENDATION USING MERGE-SPLIT APPROACH
First Claim
1. A method for making recommendations to a user, the method comprising:
- storing data relating to usage patterns of the user, wherein the data includes information as to items which were used and the context in which they were used;
clustering the data into input clusters of data points;
determining if there are any input clusters that are similar to each other;
merging the similar clusters if there are any input clusters similar to each other;
dividing any non-merged input clusters into split clusters if the split clusters would not be similar to each other; and
repeating the determining, merging, and dividing using the merged, divided, and remaining unmerged and undivided clusters as input clusters.
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Abstract
In one embodiment, data relating to usage patterns of the user is stored, wherein the data includes information as to items which were used and the context in which they were used. The data is then clustered into input clusters of data points. It is determined if there are any input clusters that are similar to each other. Similar clusters are merged if there are any input clusters similar to each other. Any non-merged input clusters are divided into split clusters if the split clusters would not be similar to each other. The determining, merging, and dividing are then repeated using the merged, divided, and remaining unmerged and undivided clusters as input clusters.
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Citations
13 Claims
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1. A method for making recommendations to a user, the method comprising:
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storing data relating to usage patterns of the user, wherein the data includes information as to items which were used and the context in which they were used; clustering the data into input clusters of data points; determining if there are any input clusters that are similar to each other; merging the similar clusters if there are any input clusters similar to each other; dividing any non-merged input clusters into split clusters if the split clusters would not be similar to each other; and repeating the determining, merging, and dividing using the merged, divided, and remaining unmerged and undivided clusters as input clusters. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. An apparatus comprising:
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an interface; and one or more processors configured to; store data relating to usage patterns of the user, wherein the data includes information as to items which were used and the context in which they were used; cluster the data into input clusters of data points; determine if there are any input clusters that are similar to each other; merge the similar clusters if there are any input clusters similar to each other; divide any non-merged input clusters into split clusters if the split clusters would not be similar to each other; and repeat the determining, merging, and dividing using the merged, divided, and remaining unmerged and undivided clusters as input clusters. - View Dependent Claims (11)
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12. A system for making recommendations to a user, the system comprising:
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means for storing data relating to usage patterns of the user, wherein the data includes information as to items which were used and the context in which they were used; means for clustering the data into input clusters of data points; means for determining if there are any input clusters that are similar to each other; means for merging the similar clusters if there are any input clusters similar to each other; means for dividing any non-merged input clusters into split clusters if the split clusters would not be similar to each other; and means for repeating the determining, merging, and dividing using the merged, divided, and remaining unmerged and undivided clusters as input clusters.
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13. A program storage device readable by a machine tangibly embodying a program of instructions executable by the machine to perform a method for making recommendations to a user, the method comprising:
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storing data relating to usage patterns of the user, wherein the data includes information as to items which were used and the context in which they were used; clustering the data into input clusters of data points; determining if there are any input clusters that are similar to each other; merging the similar clusters if there are any input clusters similar to each other; dividing any non-merged input clusters into split clusters if the split clusters would not be similar to each other; and repeating the determining, merging, and dividing using the merged, divided, and remaining unmerged and undivided clusters as input clusters.
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Specification