Abstract: The present disclosure provides systems and methods for on-device machine learning. In particular, the present disclosure is directed to an on-device machine learning platform and associated techniques that enable on-device prediction, training, example collection, and/or other machine learning tasks or functionality. The on-device machine learning platform can include a context provider that securely injects context features into collected training examples and/or client-provided input data used to generate predictions/inferences. Thus, the on-device machine learning platform can enable centralized training example collection, model training, and usage of machine-learned models as a service to applications or other clients.

Abstract: The present disclosure provides systems and methods for on-device machine learning. In particular, the present disclosure is directed to an on-device machine learning platform and associated techniques that enable on-device prediction, training, example collection, and/or other machine learning tasks or functionality. The on-device machine learning platform can include a context provider that securely injects context features into collected training examples and/or client-provided input data used to generate predictions/inferences. Thus, the on-device machine learning platform can enable centralized training example collection, model training, and usage of machine-learned models as a service to applications or other clients.

Abstract: A system and method for identifying and predicting subjective attributes for entities (e.g., media clips, movies, television shows, images, newspaper articles, blog entries, persons, organizations, commercial businesses, etc.) are disclosed. In one aspect, subjective attributes for a first media item are identified based on a reaction to the first media item, and relevancy scores for the subjective attributes with respect to the first media item are determined. A classifier is trained using (i) a training input comprising a set of features for the first media item, and a target output for the training input, the target output comprising the respective relevancy scores for the subjective attributes with respect to the first media item.

Abstract: The present disclosure provides systems and methods for on-device machine learning. In particular, the present disclosure is directed to an on-device machine learning platform and associated techniques that enable on-device prediction, training, example collection, and/or other machine learning tasks or functionality. The on-device machine learning platform can include a context provider that securely injects context features into collected training examples and/or client-provided input data used to generate predictions/inferences. Thus, the on-device machine learning platform can enable centralized training example collection, model training, and usage of machine-learned models as a service to applications or other clients.

Abstract: A processor determines metadata associated with an audio track. The processor identifies categories that are related to the audio track based on the metadata. The processor determines rankings for the categories that are related to the audio track. The ranking is indicative of a relevance of a particular category to the audio track. The processor performs a query to identify visual media for one or more of ranked categories. The visual media is related to the audio track. The processor generates a visual presentation for the audio track by selecting at least some of the visual media to include in the visual presentation.

Abstract: The present disclosure provides systems and methods for on-device machine learning. In particular, the present disclosure is directed to an on-device machine learning platform and associated techniques that enable on-device prediction, training, example collection, and/or other machine learning tasks or functionality. The on-device machine learning platform can include a context provider that securely injects context features into collected training examples and/or client-provided input data used to generate predictions/inferences. Thus, the on-device machine learning platform can enable centralized training example collection, model training, and usage of machine-learned models as a service to applications or other clients.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for providing app store search results. An example method includes responsive to a first search query directed to an app store: revising the first search query to produce a second search query different from the first search query; obtaining, from an Internet search engine, second search results responsive to the second search query; analyzing the second search results to identify apps available on the app store that are relevant to the second search query; obtaining, from the app store, first search results responsive to the first search query that identify apps available in the app store; and modifying the first search results based on analyzing the second search results.

Abstract: A system and method of recommending a bundle of content items to a user, including storing a plurality of content items in a computer system, determining a respective co-selection score for each pair of content items among the plurality of content items, the co-selection score indicating a probability that a given pair of content items among the plurality of content items will both be downloaded by a user of the computer system, and outputting, to a first user, a plurality of content items comprising a sub-set of the plurality of content items.

Abstract: The present disclosure provides a system and method for automatic clustering and recognition of software applications using metadata. The system selects and extracts visual features from software applications which are then classified, analyzed using a cluster analysis, and then used to assign the software application to a cluster group.

Abstract: A demographics analysis trains classifier models for predicting demographic attribute values of videos and users not already having known demographics. In one embodiment, the demographics analysis system trains classifier models for predicting demographics of videos using video features such as demographics of video uploaders, textual metadata, and/or audiovisual content of videos. In one embodiment, the demographics analysis system trains classifier models for predicting demographics of users (e.g., anonymous users) using user features based on prior video viewing periods of users. For example, viewing-period based user features can include individual viewing period statistics such as total videos viewed. Further, the viewing-period based features can include distributions of values over the viewing period, such as distributions in demographic attribute values of video uploaders, and/or distributions of viewings over hours of the day, days of the week, and the like.

Abstract: The present disclosure provides systems and methods for on-device machine learning. In particular, the present disclosure is directed to an on-device machine learning platform and associated techniques that enable on-device prediction, training, example collection, and/or other machine learning tasks or functionality. The on-device machine learning platform can include a context provider that securely injects context features into collected training examples and/or client-provided input data used to generate predictions/inferences. Thus, the on-device machine learning platform can enable centralized training example collection, model training, and usage of machine-learned models as a service to applications or other clients.

Abstract: The present disclosure provides systems and methods for on-device machine learning. In particular, the present disclosure is directed to an on-device machine learning platform and associated techniques that enable on-device prediction, training, example collection, and/or other machine learning tasks or functionality. The on-device machine learning platform can include a context provider that securely injects context features into collected training examples and/or client-provided input data used to generate predictions/inferences. Thus, the on-device machine learning platform can enable centralized training example collection, model training, and usage of machine-learned models as a service to applications or other clients.

Abstract: Systems and methods described herein relate to automation of video creation for an associated audio file or musical composition. In particular, a video can be generated for the audio file that includes images and videos that are compelling and contextually relevant to, and technically compatible with, the audio file.

Abstract: A system and method of recommending a bundle of content items to a user, including storing a plurality of content items in a computer system, determining a respective co-selection score for each pair of content items among the plurality of content items, the co-selection score indicating a probability that a given pair of content items among the plurality of content items will both be downloaded by a user of the computer system, and outputting, to a first user, a plurality of content items comprising a sub-set of the plurality of content items.

Abstract: Certain embodiments of the disclosed technology include systems and methods for determining the priority of a notification on a mobile device using machine learning. Other aspects of the disclosed technology include selectively displaying or emphasizing notifications based on the priority of a notification.

Abstract: A system and method for associating videos with geographic locations is disclosed. The system comprises a communication module, a location module, a tagging module and a database association module. The communication module receives a video uploaded by a content provider and a set of video data describing the video. The location module determines that the video describes a geographic location included in a geographic map based at least in part on the video data. The tagging module determines one or more travelling tags for the video based at least in part on the video data. The database association module associates the video and the one or more travelling tags with the geographic location so that the video with the one or more travelling tags is included in the geographic map.

Abstract: Certain embodiments of the disclosed technology include systems and methods for determining the priority of a notification on a mobile device using machine learning. Other aspects of the disclosed technology include selectively displaying notifications based on the priority of a notification.

Abstract: A system and method for identifying and predicting subjective attributes for entities (e.g., media clips, images, newspaper articles, blog entries, persons, organizations, commercial businesses, etc.) are disclosed. In one aspect, a first set of subjective attributes for a first entity is identified based on a reaction to the first entity. A classifier is trained on a set of input-output mappings, wherein the set of input-output mappings comprises an input-output mapping whose input is based on a feature vector for the first entity and whose output is based on the first set of subjective attributes. A feature vector for a second entity is then provided to the trained classifier to obtain a second set of subjective attributes for the second entity.

Abstract: A method includes generating a ranking model and a baseline mixing weight for each latent user category from a plurality of latent user categories based on a community preference dataset and one or more latent variables that relate the users from the community of users to the latent user categories. The method also includes generating a personalized mixing weight for each latent user category for a specified user based on an individual preference dataset, the ranking models for the latent user category, and one or more latent variables that relate the specified user to the latent user categories. The method also includes adjusting the personalized mixing weight for each latent user category for the specified user based on the baseline mixing weights, and generating ranking output for at least some objects from the plurality of objects using the personalized mixing weights and the ranking models.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for providing app store search results. An example method includes responsive to a first search query directed to an app store: revising the first search query to produce a second search query different from the first search query; obtaining, from an Internet search engine, second search results responsive to the second search query; analyzing the second search results to identify apps available on the app store that are relevant to the second search query; obtaining, from the app store, first search results responsive to the first search query that identify apps available in the app store; and modifying the first search results based on analyzing the second search results.