Abstract: A method for receiving a first user query from a user for searching an item, forming a first filter based on the first user query, and forming a first filtered item collection is provided. The method includes predicting a new query based on the first user query and a historical query log, forming a second filter for the new query, and applying the second filter to the first filtered item collection to form a second filtered item collection. Further, associating an item score to each of a plurality of items in the first and second filtered item collections, sorting the plurality of items in the first and second filtered item collections according to the item score associated to each of the plurality of items, and providing, to a user display, an item in the plurality of items in the first or second filtered item collections according to a sorting order.

Abstract: Methods for predicting network access probability of data files accessible over a computer network are provided. In one aspect, a method includes generating a primary data vector for a media file based on a stored data representation of the file, and providing the data vector for the file to an algorithm that uses past interaction information for at least one other media file from a collection of media files having a degree of similarity with the media file above a threshold similarity value. The method also includes receiving, as an output of the algorithm, a marketability score for the media file, the score indicative of a likelihood that a user will download the media file. Systems and machine-readable media are also provided.

Abstract: A method for receiving a first text in a source language is provided. The method includes associating the first text in the source language with a first vector, selected from a plurality of vectors associated with the source language in an embedded set, and identifying a second vector that is closer to the first vector than a pre-selected threshold. The second vector is associated with a second text in a target language. The method includes associating the first text in the source language with the second text in the target language, wherein the embedded set includes a first image vector for a first image and a second image vector for a second image, and returning the second text in the target language, the second text being a translation of the first text from the source language to the target language. A system configured to perform the above method is also provided.

Abstract: Methods, systems, and machine-readable media for identifying synthetic media file examples to train a supervised machine learned ranking algorithm to rank relevance of media files to a search query are provided. In one aspect, a method includes identifying a search query for a search engine for a collection of media files from previously received search queries, and selecting at least one training media file from the collection as a synthetic negative example for the search query. The method also includes providing a training set to a supervised machine learned ranking algorithm. The training set includes an identification of the search query, a copy of the training media file, and a first indicator that the training media file is a synthetic negative example for the search query. The method further includes providing, to the algorithm, the search query and the collection, and receiving, from the algorithm, a ranking of the collection.

Abstract: Computer image clustering systems and methods for conducting effective media searches by grouping multimedia documents tagged by keywords into a hierarchy of images configured to: (1) maintain a first database, (2) maintain an initial occurrence matrix, (3) maintain an occurrence matrix, (4) maintain a media file activation score for each media file in the first database, (5) generate a log version of the occurrence matrix, (6) maintain an inverse media file frequency value for each descriptive term in the first database, (7) generate a descriptive term frequency matrix and generate a list of document vectors in multidimensional space (list), and (8) organize and process each media file in the list into a high activation score category and a low activation score category.

Abstract: Methods for predicting network access probability of data files accessible over a computer network are provided. In one aspect, a method includes generating a primary data vector for a media file based on a stored data representation of the file, and providing the data vector for the file to an algorithm that uses past interaction information for at least one other media file from a collection of media files having a degree of similarity with the media file above a threshold similarity value. The method also includes receiving, as an output of the algorithm, a marketability score for the media file, the score indicative of a likelihood that a user will download the media file. Systems and machine-readable media are also provided.

Abstract: A method for receiving a first user query from a user for searching an item, forming a first filter based on the first user query, and forming a first filtered item collection is provided. The method includes predicting a new query based on the first user query and a historical query log, forming a second filter for the new query, and applying the second filter to the first filtered item collection to form a second filtered item collection. Further, associating an item score to each of a plurality of items in the first and second filtered item collections, sorting the plurality of items in the first and second filtered item collections according to the item score associated to each of the plurality of items, and providing, to a user display, an item in the plurality of items in the first or second filtered item collections according to a sorting order.

Abstract: A method for receiving an input user query from a user in any of multiple languages and creating an input user query vector for the input user query is provided. The input user query vector has a length equal to a pre-selected length determined by a dimension of a multimodal space. The method includes associating an image vector to the input user query vector in the multimodal space, the image vector having the pre-selected length, identifying, from an image database, an image associated with the image vector, and providing the image for display to the user in a results panel of a user interface. A system configured to perform the above method is also provided.

Abstract: A method for receiving a first text in a source language is provided. The method includes associating the first text in the source language with a first vector, selected from a plurality of vectors associated with the source language in an embedded set, and identifying a second vector that is closer to the first vector than a pre-selected threshold. The second vector is associated with a second text in a target language. The method includes associating the first text in the source language with the second text in the target language, wherein the embedded set includes a first image vector for a first image and a second image vector for a second image, and returning the second text in the target language, the second text being a translation of the first text from the source language to the target language. A system configured to perform the above method is also provided.

Abstract: A method for receiving an input user query from a user in any of multiple languages and creating an input user query vector for the input user query is provided. The input user query vector has a length equal to a pre-selected length determined by a dimension of a multimodal space. The method includes associating an image vector to the input user query vector in the multimodal space, the image vector having the pre-selected length, identifying, from an image database, an image associated with the image vector, and providing the image for display to the user in a results panel of a user interface. A system configured to perform the above method is also provided.

Abstract: Computer image clustering systems and methods for conducting effective media searches by grouping multimedia documents tagged by keywords into a hierarchy of images configured to: (1) maintain a first database, (2) maintain an initial occurrence matrix, (3) maintain an occurrence matrix, (4) maintain a media file activation score for each media file in the first database, (5) generate a log version of the occurrence matrix, (6) maintain an inverse media file frequency value for each descriptive term in the first database, (7) generate a descriptive term frequency matrix and generate a list of document vectors in multidimensional space (list), and (8) organize and process each media file in the list into a high activation score category and a low activation score category.

Abstract: Methods for analyzing data files to identify similar files to group for display within a limited visual space of a graphical user interface are provided. In one aspect, a method includes receiving a search query for a collection of media files, and identifying a subset of the media files from the collection that is responsive to the search query. The method also includes grouping the subset of the media files into a plurality of groups based on their visual similarity, wherein the visual similarity of each media file in the subset of media files is determined using an image vector corresponding to each media file, and providing the subset of the media files for display in their respective groups. Systems and machine-readable media are also provided.

Abstract: Methods for predicting network access probability of data files accessible over a computer network are provided. In one aspect, a method includes generating a primary data vector for a media file based on a stored data representation of the file, and providing the data vector for the file to an algorithm that uses past interaction information for at least one other media file from a collection of media files having a degree of similarity with the media file above a threshold similarity value. The method also includes receiving, as an output of the algorithm, a marketability score for the media file, the score indicative of a likelihood that a user will download the media file. Systems and machine-readable media are also provided.

Abstract: Methods, systems, and machine-readable media for identifying synthetic media file examples to train a supervised machine learned ranking algorithm to rank relevance of media files to a search query are provided. In one aspect, a method includes identifying a search query for a search engine for a collection of media files from previously received search queries, and selecting at least one training media file from the collection as a synthetic negative example for the search query. The method also includes providing a training set to a supervised machine learned ranking algorithm. The training set includes an identification of the search query, a copy of the training media file, and a first indicator that the training media file is a synthetic negative example for the search query. The method further includes providing, to the algorithm, the search query and the collection, and receiving, from the algorithm, a ranking of the collection.

Abstract: Computer image clustering systems and methods for conducting effective media searches by grouping multimedia documents tagged by keywords into a hierarchy of images configured to: (1) maintain a first database, (2) maintain an initial occurrence matrix, (3) maintain an occurrence matrix, (4) maintain a media file activation score for each media file in the first database, (5) generate a log version of the occurrence matrix, (6) maintain an inverse media file frequency value for each descriptive term in the first database, (7) generate a descriptive term frequency matrix and generate a list of document vectors in multidimensional space (list), and (8) organize and process each media file in the list into a high activation score category and a low activation score category.