Systems and methods for automatic customization of content filtering

A computing system for customization of content filtering, comprising:

• a computing system including a server operatively coupled to a plurality of content storage locations holding a plurality of modalities of content including at least two of text, image and video content, the computing system programmed to:
• receive a search string from a user;
• create a first filter based on the search string;
• perform a first search of a plurality of modalities of content stored in a plurality of locations based on the first filter;
• return a first subset of results from the first search to the user;
• receive from the user an indication of relevance for each one of the results in the first subset;
• dynamically create a second filter by:
• creating a plurality of feature spaces by extracting a plurality of feature from the plurality of modalities of content referenced by the first subset of results, the extracted features including at least two types of text features including two or more of N-Grams, latent topics, parts-of-speech, contextual content flags and contextual metadata, and at least two types of image or video features including two or more of intensity and color histograms, bag-of-words scale-invariant feature transform (SIFT), border/interior classification, dense SIFT, GIST, histogram orientation gradients HOG, a number of faces, and a pedestrian confidence score;
• reducing a dimensionality of each feature space in the plurality of feature spaces;
• and
• applying a classifier trained on the user indication of relevance or non-relevance to the reduced feature space;
• and
• return a second subset of results to the user generated by applying the classifier.