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: Described herein is a system for recommending music using a music taste profile. The music taste profile includes information about musical taste of a particular user that can be used to provide music recommendation(s) targeted to that particular user. The system includes a recommendation component that makes a music recommendation(s) based on the music taste profile. The recommendation component can utilize an algorithm to make the music recommendation. With consent of the user, a music taste profile modification component can modify the music taste profile based on an inference from information regarding a music-related interest of the user received from a source (e.g., information obtained about a web browsing search history of the user, signal(s) provided by a digital assistant regarding information requested by the user and/or from a home automation device).

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: Examples are disclosed that relate to the automatic creation of playlists. For example, one disclosed example provides, on a computing device comprising a processor, a method for generating playlists of content items from a collection of content items. The method comprises determining a set of candidate playlists, where each candidate playlist may include a different plurality of content items selected from the collection of content items. The method further comprises assigning a score to each candidate playlist in the set, and presenting via a display a subset of candidate playlists. Each candidate playlist in the subset may be selected from the set of candidate playlists based on the assigned score of the candidate playlist.

Abstract: Example apparatus and methods concern automatically creating editorial content for albums that are to be recommended in a computerized music marketplace. An album may be selected for recommendation after analyzing consumption data (e.g., sales, downloads, samples), and popularity data (e.g., chart rankings, critical reviews). The popularity data may also include data mined from sources including search engines and social media sites. An overall recommendation score may be a weighted function of consumption data and popularity data. A customized recommendation score may be a weighted function of the overall recommendation score influenced by regional considerations, demographic considerations, and other considerations. The editorial content may include art and text and may be customized based on regional considerations, demographic considerations, and based on other art or text that will appear at the same time as the editorial content.

Abstract: One or more multimedia recommendation candidates are identified for a user. For each of the one or more multimedia recommendation candidates, a similarity between a multimedia artist associated with that multimedia recommendation candidate and a preferred artist of the user is determined That multimedia recommendation candidate is recommended to the user if the similarity exceeds a threshold.

Abstract: Example apparatus and methods concern automatically creating editorial content for albums that are to be recommended in a computerized music marketplace. An album may be selected for recommendation after analyzing consumption data (e.g., sales, downloads, samples), and popularity data (e.g., chart rankings, critical reviews). The popularity data may also include data mined from sources including search engines and social media sites. An overall recommendation score may be a weighted function of consumption data and popularity data. A customized recommendation score may be a weighted function of the overall recommendation score influenced by regional considerations, demographic considerations, and other considerations. The editorial content may include art and text and may be customized based on regional considerations, demographic considerations, and based on other art or text that will appear at the same time as the editorial content.

Abstract: Example apparatus and methods concern automatically creating editorial content for albums that are to be recommended in a computerized music marketplace. An album may be selected for recommendation after analyzing consumption data (e.g., sales, downloads, samples), and popularity data (e.g., chart rankings, critical reviews). The popularity data may also include data mined from sources including search engines and social media sites. An overall recommendation score may be a weighted function of consumption data and popularity data. A customized recommendation score may be a weighted function of the overall recommendation score influenced by regional considerations, demographic considerations, and other considerations. The editorial content may include art and text and may be customized based on regional considerations, demographic considerations, and based on other art or text that will appear at the same time as the editorial content.

Abstract: Example apparatus and methods concern automatically creating editorial content for albums that are to be recommended in a computerized music marketplace. An album may be selected for recommendation after analyzing consumption data (e.g., sales, downloads, samples), and popularity data (e.g., chart rankings, critical reviews). The popularity data may also include data mined from sources including search engines and social media sites. An overall recommendation score may be a weighted function of consumption data and popularity data. A customized recommendation score may be a weighted function of the overall recommendation score influenced by regional considerations, demographic considerations, and other considerations. The editorial content may include art and text and may be customized based on regional considerations, demographic considerations, and based on other art or text that will appear at the same time as the editorial content.