Situation-aware recommendation using limited cluster sizes
First Claim
1. A computer-implemented 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 clusters of data points, wherein the number of data points per cluster is artificially limited to a preset maximum number of data points per cluster;
determining a centroid for each of the clusters;
selecting clusters similar to a current context of the user by comparing a data point representing the current context of the user to one or more of the centroids; and
computing, for each of one or more items, a probability that the user wishes to use the corresponding item, based on the selected similar clusters, wherein the probabilities are used for recommending one or more of the items.
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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 clusters of data points, wherein the number of data points per cluster is limited based on a preset value. Then a centroid is determined for each of the clusters. Clusters similar to the current context of the user are then selected by comparing a data point representing the current context of the user to one or more of the centroids. Then, for each of the one or more items, a probability that the user wishes to use the corresponding item is computed, based on the selected similar clusters, wherein the probabilities are used to recommend one or more of the items.
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Citations
16 Claims
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1. A computer-implemented 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 clusters of data points, wherein the number of data points per cluster is artificially limited to a preset maximum number of data points per cluster; determining a centroid for each of the clusters; selecting clusters similar to a current context of the user by comparing a data point representing the current context of the user to one or more of the centroids; and computing, for each of one or more items, a probability that the user wishes to use the corresponding item, based on the selected similar clusters, wherein the probabilities are used for recommending one or more of the items. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. 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 clusters of data points, wherein the number of data points per cluster is artificially limited to a preset maximum number of data points per cluster; determine a centroid for each of the clusters; select clusters similar to a current context of the user by comparing a data point representing the current context of the user to one or more of the centroids; and compute, for each of one or more items, a probability that the user wishes to use the corresponding item, based on the selected similar clusters, wherein the probabilities are used to recommend one or more of the items. - View Dependent Claims (13, 14)
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15. 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 clusters of data points, wherein the number of data points per cluster is artificially limited to a preset maximum number of data points per cluster; means for determining a centroid for each of the clusters; means for selecting clusters similar to a current context of the user by comparing a data point representing the current context of the user to one or more of the centroids; means for computing, for each of one or more items, a probability that the user wishes to use the corresponding item, based on the selected similar clusters, wherein the probabilities are used to recommend one or more of the items; and a processor coupled to the means for storing, means for clustering, means for determining, means for selecting, and means for computing.
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16. A non-transitory 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 clusters of data points, wherein the number of data points per cluster is artificially limited to a preset maximum number of data points per cluster; determining a centroid for each of the clusters; selecting clusters similar to a current context of the user by comparing a data point representing the current context of the user to one or more of the centroids; and computing, for each of one or more items, a probability that the user wishes to use the corresponding item, based on the selected similar clusters.
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Specification