Abstract: Systems and methods are provided for personalizing new song suggestions for a user. A plurality of users provides thumbs up and thumbs down ratings for a pair of songs (song A and B). A first aggregate rating for song B is calculated based on ratings given to song A by all of the users that gave a like rating to song A. Similarly, a second aggregate rating is calculated for song B based on ratings given to song B by all users that gave a dislike rating to song A. Then a user that has not previously rated song B, supplies a rating for song A. A prediction is performed of how the user will rate song B based on the user's rating of song A. For example, the first aggregate rating for song B is used if the user rated song A positively.

Abstract: A media playlist is generated in which media items have a tempo matching a target tempo. The content server receives a seed value and a tempo signal from a client device. The tempo signal indicates a target tempo specified by the user. The content server calculates a target tempo range associated with the target tempo and generates a filtered playlist of media items based on the seed value and the target tempo range. The media items in the in the filtered playlist have tempos within the target tempo range. The content server provides the media items in the filtered playlist to the client device.

Abstract: A content server generates media playlists for social events. The content server receives a seed value describing desired characteristics of media to present during a social event, and event parameters describing conditions for selecting media items to generate a media playlist for the social event. The content server selects media items based on the event parameters and the seed value, and uses the selected media items to generate the media playlist for the social event. The media items in the playlist are retrieved from a media database and provided to a media player for presentation at the social event.

Abstract: A digital content provider that provides content channels to users via a computer network selects an artist message. The content provider retrieves a predictive model trained to calculate an artist affinity score indicating a likelihood that a given user will provide positive feedback to a given artist message. The predictive model is trained, using machine learning, based on feedback from users for previous plays of artist messages. The content provider retrieves artist features describing the artist who recorded the artist message, and user features describing a candidate user. The content provider calculates an artist affinity score for the candidate user and selected artist message using the predictive model. The content provider determines to play a content item by the artist to the candidate user, determines to play the selected artist message associated with the content item based on the artist affinity score, and transmits the artist message and content item.

Abstract: A content server system provides content items to a plurality of users in a content stream. The content server system provides an interface to collect feedback from the plurality of users on the content items in the content stream. Based on the collected feedback the content server system generates a feedback model that predicts the likelihood of receiving feedback on a content item at any given time in a streaming session. The content server system then selects a content item in need of feedback and, using the model, determines a time at which the content item is more likely to receive feedback. The content server system then provides the content item to the user at the determined time.

Abstract: A system and a method are disclosed for recommending podcasts. In an embodiment, the system detects a podcast request, and responsively determines whether a profile of the user indicates consumption by the user of podcasts. Responsive to determining that the profile does not indicate consumption by the user of podcasts, the system determines music preferences of the user based on data of the profile, and generates a podcast seed based on the music preferences. The system selects a podcast recommendation based on the podcast seed, and provides the podcast recommendation to the user. The system receives a selection of the podcast recommendation by the user, and responsively plays back a podcast corresponding to the podcast recommendation.

Abstract: A content server system provides a client device with content, such as an audio stream. Using various techniques, an estimate is made of the actual size of an audience associated with the provided content, rather than assuming that the audience is limited to a single user of the client device. The estimate may be made by the content server system, the client device, or the content server system and the client device collectively. Using the estimate of actual size of the audience, the content server system can take actions appropriate for the audience, such as providing advertisements appropriate for the size of the audience and for the collective characteristics of the audience members. The estimate of the actual audience size additionally allows the content server system to be compensated more precisely for any advertisements provided to that audience.

Abstract: A content server uses a form of artificial intelligence such as machine learning to identify audio content with musicological characteristics. The content server obtains an indication of a music item presented by a client device and obtains reference music features describing musicological characteristics of the music item. The content server identifies candidate audio content associated with candidate music features. The candidate music features are determined by analyzing acoustic features of the candidate audio content and mapping the acoustic features to music features according to a music feature model. Acoustic features quantify low-level properties of the candidate audio content. One of the candidate audio content items is selected according to comparisons between the candidate music features of the candidate audio advertisements and the reference music features of the music item. The selected audio content is provided the client device for presentation.

Abstract: A content server provides a client device with audio content including an audio advertisement, which is provided in response to receiving a request for digital audio content from a client device associated with a user. The content server obtains user information about the user and retrieves advertisement text received from an advertiser, which are used to generate a personalized text advertisement. The personalized text advertisement is generated according to an advertisement template specifying an ordered combination of text components. The personalized text advertisement includes the received advertisement text, user information text selected from the obtained user information, and template text. The client device is provided with an advertisement based on the personalized text advertisement and is configured to play an audio version of the personalized text advertisement. The audio advertisement is generated using a text-to-speech algorithm at the client device or at the content server.

Abstract: A digital content provider that provides content channels to users via a computer network selects an artist message. The content provider retrieves a predictive model trained to calculate an artist affinity score indicating a likelihood that a given user will provide positive feedback to a given artist message. The predictive model is trained, using machine learning, based on feedback from users for previous plays of artist messages. The content provider retrieves artist features describing the artist who recorded the artist message, and user features describing a candidate user. The content provider calculates an artist affinity score for the candidate user and selected artist message using the predictive model. The content provider determines to play a content item by the artist to the candidate user, determines to play the selected artist message associated with the content item based on the artist affinity score, and transmits the artist message and content item.

Abstract: A content mediating server receives from a client device a request for a next content item to be selected based on a content event relating to a current content item. The content mediating server generates requests for content from content providing servers providing content of corresponding types, and receives from each content providing server a content table relating content events in a set of content events to content items. For each content event in the set, the content mediating server selects, from the received tables, a content item, generates a composite content table relating each content event in the set to the selected content item, and transmits the composite table to the client device. The client device, in response to an observed content event relating to the current content item, selects the content item in the composite table corresponding to the observed content event as the next content item.

Abstract: Systems and methods are provided for personalizing new song suggestions for a user. A plurality of users provides thumbs up and thumbs down ratings for a pair of songs (song A and B). A first aggregate rating for song B is calculated based on ratings given to song A by all of the users that gave a like rating to song A. Similarly, a second aggregate rating is calculated for song B based on ratings given to song B by all users that gave a dislike rating to song A. Then a user that has not previously rated song B, supplies a rating for song A. A prediction is performed of how the user will rate song B based on the user's rating of song A. For example, the first aggregate rating for song B is used if the user rated song A positively.

Abstract: A content server provides customized media items to present to a user using vehicle information. The content server receives environmental information, user feedback for presented media items, and vehicle information for an associated vehicle from a client device associated with a user. The client device receives the vehicle information from a vehicle platform of the vehicle. The content server analyzes the environmental information and user feedback to create a user profile for the user. The content server also analyzes the vehicle information to create a vehicle profile for the associated vehicle. Using the user profile and the vehicle profile, the content server selects media items to send to the client device to present to the user.

Abstract: A content server generates media playlists for social events. The content server receives a seed value describing desired characteristics of media to present during a social event, and event parameters describing conditions for selecting media items to generate a media playlist for the social event. The content server selects media items based on the event parameters and the seed value, and uses the selected media items to generate the media playlist for the social event. The media items in the playlist are retrieved from a media database and provided to a media player for presentation at the social event.

Abstract: A content server generates a media channel including a plurality of media items. A request for a media channel is received from a client device of a user, the request including a free-form media input seed identifying a plurality of ambiguous entities. A plurality of media playlists of media items is determined where each media playlist is associated with a corresponding one of the plurality of ambiguous entities identified by the free-form media input seed. The plurality of media playlists are combined into the media channel that includes media items selected from among the plurality of media playlists. The media channel is provided to the client device.

Abstract: An Internet radio service supports song dedications. A user is able to listen to music on an Internet radio station and dedicate a song to another person. A notification alert is generated to notify the intended recipient that a dedication has been made. The intended recipient of the dedication is able to then access the dedicated song and any accompanying message. A public acknowledgement of the song dedication may also be made in social media.

Abstract: A content server system provides a client device with content including an audio advertisement with personalized background music. The content server selects a vocal advertisement received from an advertiser for presentation to a user of a client device. The content server obtains content data describing audio content provided to the client device as part of the stream of audio content. The content server obtains content data describing audio content provided to the client device as part of the stream of audio content. The content server identifies candidate background music items and selects background music from the candidate background music items based on the obtained content data. The client device is provided with the selected background music and the selected vocal advertisement. The client device plays an audio advertisement comprising the background music played concurrently with the vocal advertisement.