Abstract: In one embodiment, an audio signal for an audio track is received and segmented into a plurality of segments of the audio signal. The plurality of segments of audio are input into a classification network that is configured to predict output values based on a plurality of genre and mood combinations formed from different combinations of a plurality of genres and a plurality of moods. The classification network predicts a set of output values for the plurality of segments, each of the set of output values corresponding to one or more the plurality of genre and mood combinations. One or more of the plurality of genre and mood combinations are assigned to the audio track based on the set of output values for one or more of the plurality of segments.

Abstract: Aspects of the technology described herein use acoustic features of a music track to capture information for a recommendation system. The recommendation can work without analyzing label data (e.g., genre, artist) or usage data for a track. For each audio track, a descriptor is generated that can be used to compare the track to other tracks. The comparisons between track descriptors result in a similarity measure that can be used to make a recommendation. In this process, the audio descriptors are used directly to form a track-to-track similarity measure between tracks. By measuring the similarity between a track that a user is known to like and an unknown track, a decision can be made whether to recommend the unknown track to the user.

Abstract: A recommender system that represents items in a catalog by first feature vectors in a first vector space based on first characteristics of the items and second feature vectors in a second vector space based on second characteristics of the items different from the first characteristics and maps a feature vector defined in the first vector space for an item to a vector in the second vector space to provide recommendations based on the item.

Abstract: Aspects disclosed herein may utilize neural embedding techniques to model session activity. A dataset may be collected from on online market place, such as an app store. The data set may include one or more user sessions comprising sequential click actions and/or item purchases. Models may be generated to represent session activity and, therefore, may be utilized for contextual recommendations of apps in an online app store. As such, the various aspects disclosed herein may also generate purchase predictions based on click-purchase relations in a sequence. The item similarities and purchase predictions may be used to provide real-time aid to users navigating an online marketplace.

Abstract: A playlist generator that utilizes multiple data sources to rank each track within a set of candidate tracks to enable selection of candidate tracks according to the ranking. Candidate tracks are each scored according to one or more features, such as acoustic similarity and/or similar usage patterns of the candidate track or artist of the candidate track to a current or previously played track or artist. Each feature is weighted according to historical listening patterns surrounding a user-selected playlist seed artist. The weighting may also be further corrected according to historical listening patterns of the particular user. When historical usage data related to a particular seed artist is limited, more generalized historical usage data related to a higher level in a genre hierarchy may be used.

Abstract: A method for producing item recommendations for user consumption from usage data of items as they are consumed in combinations or baskets; breaking the baskets into positive pairs of items appearing in the baskets; finding negative pairs of items appearing relatively frequently in the baskets but not in the positive pairs; embedding all the items of the global catalog or universal set into a latent space such that items appearing together more often in the positive pairs are relatively close together and items appearing together in the negative pairs are relatively far apart; obtaining a selection from a user of a first item for consumption and providing to the user at least one suggestion of a second item for further consumption, the second item not being identical with the first item, the second item being an item located most closely in the latent space to the first item for consumption.

Abstract: A video upload system comprising a web server adapted to receive, from a user, a video file and a selection of a plurality of video sharing websites; and a video upload engine adapted to log in to said plurality of video sharing websites and to upload said video to said plurality of video sharing websites, based on the selection received from said user.