User classification based upon images
One or more systems and/or methods for providing content to a user are provided. An image, associated with a user, may be evaluated utilizing an image classifier to identify an object within the image. The object may be utilized to identify a predicted class for the user. In an example, the predicted class may correspond to a life event (e.g., graduating college, having a baby, buying a house, etc.) and/or a life stage (e.g., adolescence, retirement, etc.). Locational information (e.g., a geotag) for the image may be evaluated to determine an image location (e.g., a location where the image was generated). Responsive to the image location corresponding to a home location of the user, the predicted class may be determined to be a class associated with the user. Content (e.g., promotional content) may be selected from a content repository based upon the class and subsequently provided to the user.
1. A method of providing content to a user, comprising:
identifying a predicted class based upon an object within an image associated with a user;
evaluating locational information for the image to determine an image location;
determining a location of the user comprising at least one of:
evaluating locational information for one or more additional images associated with the user utilizing a leader-follower algorithm to determine the location based upon a locational grouping of the one or more additional images over a set timespan; or
responsive to a total number of images in a first cluster of images exceeding an image count threshold and a timespan for the images in the first cluster exceeding a timespan threshold, determining that a first location associated with the first cluster corresponds to the location of the user;
responsive to the image location corresponding to the location of the user, determining that the predicted class is a class associated with the user;
selecting content from a content repository based upon the class; and
providing the content to the user.
2. The method of claim 1 , comprising:
obtaining the image from at least one of a social media community or a microblogging community.
3. The method of claim 1 , comprising:
obtaining the image from a workgroup community.
4. The method of claim 1 , comprising:
determining a confidence score for the predicted class, the identifying the predicted class as a class associated with the user comprising identifying the predicted class as the class based upon the confidence score exceeding a confidence threshold.
5. The method of claim 4 , the determining a confidence score comprising:
comparing the object to a set of known objects to determine a match score for the object; and
responsive to the match score exceeding a threshold amount, increasing the confidence score for the predicted class.
6. The method of claim 4 , the determining a confidence score comprising:
evaluating an individual within the image utilizing a facial recognition technique to determine an identity of the individual; and
responsive to the identity of the individual corresponding to the user, increasing the confidence score for the predicted class.
7. The method of claim 1 , the class comprising at least one of:
an athletic class, a college class, a school class, a graduate class, an engagement class, a wedding class, a parent class, a new homeowner class, a career class, a hobby class, or a pet owner class.
8. The method of claim 1 , comprising:
utilizing an image classifier to identify the object within the image, the image classifier comprising a non-linear classifier utilizing a convolutional neural network configured to transform image elements.
9. The method of claim 8 , comprising:
configuring the image classifier to transform image elements through a series of layers, wherein each layer extracts features from an output of a previous layer.
10. The method of claim 9 , the series of layers comprising:
at least one of a pooling layer, a rectified linear unit layer, a convolutional layer, or fully connected layer.
11. The method of claim 1 , comprising:
utilizing an image classifier to identify the object within the image;
receiving feedback associated with at least one of the predicted class or the object from the user; and
training the image classifier based upon the feedback.
12. The method of claim 1 , comprising:
receiving feedback associated with at least one of the predicted class or the object from the user; and
responsive to the feedback indicating an improper identification for at least one of the predicted class or an image feature, prompting the user to provide a proper identification of at least one of the predicted class or the image feature.
13. The method of claim 1 , comprising:
aggregating pixel attributes of the image into patch-level features;
evaluating the patch-level features to determine aggregated patch-level features;
evaluating the aggregated patch-level features to identify one or more image features for the image; and
evaluating the one or more image features to identify the object.
14. The method of claim 1 , comprising:
obtaining the image from an online forum community.
15. The method of claim 1 , comprising:
obtaining the image from an image sharing community.
16. A system for providing content to a user, comprising:
a processor; and
memory comprising instructions that, when executed by the processor, perform operations comprising:
identifying a predicted class based upon an object within an image associated with a user;
evaluating locational information for the image to determine an image location:
evaluating locational information for one or more additional images associated with the user utilizing a leader-follower algorithm to determine a location of the user based upon a locational grouping of the one or more additional images over a set timespan;
responsive to the image location corresponding to the location of the user, determining that the predicted class is a class associated with the user;
selecting content from a content repository based upon the class; and
providing the content to the user.
17. The system of claim 16 , the operations comprising:
aggregating pixel attributes of the image into patch-level features;
evaluating the patch-level features to determine aggregated patch-level features;
evaluating the aggregated patch-level features to identify one or more image features for the image; and
evaluating the one or more image features to identify the object within the image.
18. The system of claim 16 , the operations comprising:
determining a confidence score for the predicted class, the determining that the predicted class is the class based upon the confidence score exceeding a confidence threshold.
19. The system of claim 16 , the operations comprising:
training an untrained image classifier component using a training image database to create an image classifier component configured to classify the image.
20. A non-transitory computer readable medium comprising computer executable instructions that when executed by a processor perform a method for providing content to a user, comprising:
evaluating an image repository associated with a user to identify a set of images comprising locational information;
grouping one or more images of the set of images into a first cluster based upon the one or more images corresponding to a first location;
determining a total number of images in the first cluster;
determining a timespan for the images in the first cluster;
responsive to the total number of images in the first cluster exceeding an image count threshold and the timespan for the images in the first cluster exceeding a timespan threshold, determining that the first location corresponds to a location of the user; and
providing content to the user based upon the location.