Feature vector clustering
First Claim
1. A method for determining a user has an interest in a Kind comprising:
- identifying a dimension comprised within a user feature vector of a user, the dimension comprised within the user feature vector having a user interest probability value above an interest threshold value, the user interest probability value specifying a probability that the user has an interest in a characteristic represented by the dimension;
identifying a Kind feature vector of a Kind based upon the Kind feature vector comprising the dimension, the dimension comprised within the Kind feature vector having a Kind relevancy probability value above a relevancy threshold value, the Kind relevancy probability value specifying a probability that the Kind relates to the characteristic represented by the dimension, the Kind representing a non-digital entity; and
determining that the user has an interest in the Kind based upon the Kind relevancy probability value being above the relevancy threshold value and the user interest probability value being above the interest threshold value.
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Accused Products
Abstract
One goal of computer services (e.g., email, web pages, blogs, advertisements, etc.) is to provide a user with Kinds (digital representations of everyday things) that may be relevant and interesting to the user. Users and Kinds may be plotted within a multidimensional matrix as feature vectors based upon their respective characteristics. An unsupervised clustering technique may be executed upon the matrix to create a mathematical cluster of feature vectors having similar characteristics. For example, a clothing cluster may comprise a dress Kind, a shoe Kind, a wool Kind, a watch Kind, etc. because the unsupervised clustering technique may determine these Kinds are plotted within the matrix in such a way that they have similar characteristics relating to clothing. The unsupervised clustering technique may also be utilized in determining which Kinds may be relevant to a user given a particular context with which a user is engaged with a computer resource.
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Citations
20 Claims
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1. A method for determining a user has an interest in a Kind comprising:
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identifying a dimension comprised within a user feature vector of a user, the dimension comprised within the user feature vector having a user interest probability value above an interest threshold value, the user interest probability value specifying a probability that the user has an interest in a characteristic represented by the dimension; identifying a Kind feature vector of a Kind based upon the Kind feature vector comprising the dimension, the dimension comprised within the Kind feature vector having a Kind relevancy probability value above a relevancy threshold value, the Kind relevancy probability value specifying a probability that the Kind relates to the characteristic represented by the dimension, the Kind representing a non-digital entity; and determining that the user has an interest in the Kind based upon the Kind relevancy probability value being above the relevancy threshold value and the user interest probability value being above the interest threshold value. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A system for determining a user has an interest in a Kind comprising:
a relevancy component configured to; identify a dimension comprised within a user feature vector of a user, the dimension comprised within the user feature vector having a user interest probability value above an interest threshold value, the user interest probability value specifying a probability that the user has an interest in a characteristic represented by the dimension; identify a Kind feature vector of a Kind based upon the Kind feature vector comprising the dimension, the dimension comprised within the Kind feature vector having a Kind relevancy probability value above a relevancy threshold value, the Kind relevancy probability value specifying a probability that the Kind relates to the characteristic represented by the dimension, the Kind representing a non-digital entity; and determine that the user has an interest in the Kind based upon the Kind relevancy probability value being above the relevancy threshold value and the user interest probability value being above the interest threshold value, at least some of the relevancy component implemented at least in part via a processing unit. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19)
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20. A computer-readable memory comprising processor-executable instructions that when executed perform a method for determining a user has an interest in a Kind comprising:
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identifying a dimension comprised within a user feature vector of a user, the dimension comprised within the user feature vector having a user interest probability value above an interest threshold value, the user interest probability value specifying a probability that the user has an interest in a characteristic represented by the dimension; and identifying a Kind feature vector of a Kind based upon the Kind feature vector comprising the dimension, the dimension comprised within the Kind feature vector having a Kind relevancy probability value above a relevancy threshold value, the Kind relevancy probability value specifying a probability that the Kind relates to the characteristic represented by the dimension, the Kind representing a non-digital entity.
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Specification