System and method for creating a preference profile from shared images

1. A system, comprised of at least one data processor connected with at least one memory that stores software instructions, where execution of the software instructions by the at least one data processor causes the system to perform operations to:

for a subject category of interest having a plurality N of sub-categories, where N is an integer greater than one, train over a first set of images a plurality of image classifiers to yield a plurality of pre-trained classifiers (C 1 . . . C N ), where each individual one of the pre-trained classifiers is trained to recognize one sub-category of the plurality of sub-categories;

obtain a second set of images (i=1 . . . M) from at least one social media website that a person has shared /posted /pinned or otherwise expressed an interest in, where M represents a total number of images in the second set of images;

apply at least some of the pre-trained classifiers (C 1 . . . C N ) to each image i in the second set of images;

obtain a classification score vector S i =[s 1 . . . , s N ] for image i(i=1 . . . M), where s j indicates a classification score output from a classifier C j by applying it to image i;

aggregate the M classification score vectors over all of the images of the second set of images;

derive a data construct that is descriptive of a preference profile of the person with respect to the sub-categories of the category of interest as a probability distribution over the N sub-categories; and

characterize by how much the person's profile differs from an average profile of a group of persons by assigning the person to a segment, where P a represents a probability vector for the person, and calculating an average P segment _ i for each segment i, where a degree to which the person is a member of the segment i is a dot product of the two vectors:

P a ·P segment _ i /(|P a ||P segment _ i |).

2. The system of claim 1 , where the category is fashion, and where the sub-categories are each a particular fashion style.

3. The system as in claim 1 , where aggregation of the M classification score vectors comprises finding an average of all M classification score vectors:

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4. The system of claim 1 , where aggregation of the M classification score vectors comprises finding a maximum of all M classification score vectors:

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5. The system of claim 1 , where aggregation of the M classification score vectors comprises thresholding each classification score to obtain a binary output and counting the number of classification score vectors falling into each sub-category.

6. The system of claim 1 , where derivation of the profile of the person comprises converting confidence scores in the aggregated classification score vector into a probability, where for each sub-category the person's probability of preferring that sub-category equals the percentage of that person's second set of images containing that sub-category.

7. A system, comprised of at least one data processor connected with at least one memory that stores software instructions, where execution of the software instructions by the at least one data processor causes the system to perform operations to:

obtain from an online social media site a plurality of instances of images of objects associated with a person;

analyze with a data processor the plurality of instances of the images with a plurality of predetermined style classifiers to obtain a score for each image for each style classifier; and

determine with the data processor, based on the obtained scores, a likely preference of the person for a particular style of object; where execution of the software instructions by the at least one data processor causes the system to further perform an initial operation of, for a subject category of interest having a plurality N of sub-categories, where N is an integer greater than one, training over a first set of images a plurality of image classifiers to yield a plurality of pre-trained classifiers (C 1 . . . C N ), where each individual one of the pre-trained classifiers is trained to recognize one sub-category of the plurality of sub-categories;

where the operation to obtain further comprises obtaining a second set of images (i=1 . . . M) from at least one social media website that a person has shared /posted /pinned or otherwise expressed an interest in, where M represents a total number of images in the second set of images;

and where the operation to analyze and determine comprise:

applying at least some of the pre-trained classifiers (C 1 . . . C N ) to each image i in the second set of images;

obtaining a classification score vector S i =[s 1 . . . , s N ] for image where i(i=1 . . . M),where s j indicates a classification score output from a classifier C j by applying it to image i;

aggregating the M classification score vectors over all of the images of the second set of images;

deriving a data construct that is descriptive of a preference profile of the person with respect to the sub-categories of the category of interest as a probability distribution over the N sub-categories; and

characterize by how much the person's profile differs from an average profile of a group of persons by assigning the person to a segment, where P a represents a probability vector for the person, and calculating an average P segment i, for each segment i, where a degree to which the person is a member of the segment i is a dot product of the two vectors:

P a ·P segment _ i /(|P a ||P segment _ i |).

8. The system of claim 7 , where the operation to aggregate the M classification score vectors comprises one of:

finding an average of all M classification score vectors:

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 and

finding a maximum of all M classification score vectors:

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9. The system of claim 7 , where the operation to aggregate the M classification score vectors comprises thresholding each classification score to obtain a binary output and counting the number of classification score vectors falling into each sub-category.

10. The system of claim 7 , where deriving the profile of the person comprises converting confidence scores in the aggregated classification score vector into a probability, where for each sub-category the person's probability of preferring that sub-category equals the percentage of that person's second set of images containing that sub-category.