Spotify Ab

Santiago Gil

Ian Anderson

Clay Gibson

Oguz Semerci

Margreth Mpossi

Scott Wolf

Will Shapiro

Benjamin Lacker

A server system accesses a profile of a user of the media-providing service. The profile indicates a demographic group of the user. For each media item of a plurality of media items, the server system determines a year associated with the media item. The server system selects content for the user based at least in part on an affinity of members of the demographic group, as compared to members of other demographic groups, for content from the year associated with the media item. The server system provides the selected content to a client device associated with the user.

Methods and systems for personalizing user experience based on nostalgia metrics

A server system accesses a profile of a user of the media-providing service. The profile indicates a demographic group of the user. For each track of a plurality of tracks, the server system determines a year associated with the track. The server system selects content for the user based at least in part on an affinity of members of the demographic group, as compared to members of other demographic groups, of music from the year associated with the track. The server system provides the selected content to a client device associated with the user.

An electronic device associated with a media-providing service has one or more processors and memory storing instructions for execution by the one or more processors. For each track of a plurality of tracks consumed by a user of the media-providing service, the electronic device receives, over a time window, an indication of user selection of the track to compile a listening history of the user and determines, based on the listening history of the user with the media-providing service, whether the track has previously been consumed by the user. The electronic device determines, for the user, a discovery score corresponding to an average preference of the user for selecting previously unconsumed tracks. In response to determining that an amount of use of the media-providing service by the user does not satisfy a use threshold, the electronic device forgoes recommending tracks to the user based on the discovery score.

Methods and systems for personalizing user experience based on use of service

An electronic device associated with a media-providing service assigns one or more characteristics of media items to at least one respective personality trait of a plurality of personality traits. The media items are provided by the media-providing service. The electronic device assigns one or more user behaviors to a first personality trait and tracks behavior of a user. The electronic device determines that a tracked behavior of the user corresponds to a first user behavior of the one or more user behaviors and assigns the first personality trait to the user based at least in part on determining that the tracked behavior of the user corresponds to the first user behavior. The electronic device provides personalized content to the user in accordance with a determination that the degree to which the tracked behavior of the user corresponds to the first user behavior satisfies a threshold.

Methods and systems for personalizing user experience based on personality traits

A server system accesses a listening history of a user of the media-providing service, where the user is in a demographic group. For each track of a plurality of tracks in the listening history of the user, the server system determines a year associated with the track and calculates a first metric based at least in part of an affinity of members of a demographic group, as compared to members of other demographic groups, of music from the year associated with the track. The server system selects content for the user based on the first metric and provides the selected content to a client device associated with the user.

A server system accesses a listening history of a user of the media-providing service, where the user is in a demographic group. For each track of a plurality of tracks in the listening history of the user, the server system calculates a first metric based at least in part on an affinity of members of the demographic group, as compared to members of other demographic groups, for the track. The server system selects content for the user based on the first metric and provides the selected content to a client device associated with the user.

An electronic device accesses a listening history of a user of a media-providing service, and for each characteristic of a set of characteristics describing tracks provided by the media-providing service, determines a measure of divergence with respect to listening contexts for the listening history of the user. The electronic device identifies a characteristic that has a respective measure of divergence that satisfies a threshold indicative of a lack of uniformity in the listening history with respect to the listening contexts. The electronic device determines a representative value of the identified characteristic for a listening context corresponding to a current listening context of the user. The electronic device provides personalized content to a client device of the user based on the current listening context and representative value of the identified characteristic.

Methods and systems for personalizing content in accordance with divergences in a user's listening history

A server system divides respective groups of a plurality of groups of sessions for multiple users of the media-providing service into respective pluralities of clusters. The server system tracks user interaction with a client device during a user session and identifies the user session as belonging to a first cluster of the pluralities of clusters based at least in part on the user interaction. The server system personalizes content for the user session using one or more content criteria associated with the first cluster, in accordance with identifying that the user session belongs to the first cluster.

Methods and systems for session clustering based on user experience, behavior, and interactions

An electronic device is associated with a media-providing service. The electronic has one or more processors and memory storing instructions for execution by the one or more processors. For each track of a plurality of tracks consumed by a user of the media-providing service, the electronic device determines, based on a listening history of the user with the media-providing service, whether the track has previously been consumed by the user. The electronic device determines, for the user, a discovery score based on a number of the plurality of tracks determined to not have been previously consumed by the user. The electronic device provides personalized content to the user based on the discovery score.

Methods and systems for personalizing user experience based on discovery metrics

An electronic device associated with a media-providing service has one or more processors and memory storing instructions for execution by the one or more processors. The electronic device accesses a listening history of a user of the media-providing service. Based at least in part on the listening history of the user, the electronic device assigns a first personality trait of a plurality of personality traits to the user. The electronic device provides personalized content to the user based on the first personality trait.

A server system of a media-providing service has one or more processors and memory that stores instructions for execution by the one or more processors. The server system accesses a listening history of a user that specifies tracks consumed by the user. The tracks have respective characteristics that describe the tracks. The server system determines for the user, using the listening history, a diversity score based on variation of the characteristics across the tracks consumed by the user. The server system provides personalized content to the user based on the diversity score.

Methods and systems for personalizing user experience based on diversity metrics

A server system accesses a listening history of a user of the media-providing service, where the user is in a demographic group. For each track of a plurality of tracks in the listening history of the user, the server system calculates a first metric based at least in part on an affinity of members of the demographic group, as compared to members of other demographic groups, for the track. The server system averages the first metrics for the plurality of tracks in the listening history of the user to determine a second metric. In accordance with a determination that the second metric satisfies a threshold, the server system selects content for the user and provides the selected content to a client device associated with the user.

A server system sorts a plurality of sessions for multiple users of a media-providing service into a plurality of groups by applying one or more sorting rules to one or more session characteristics for the plurality of sessions. The server system divides respective groups of the plurality of groups into respective pluralities of clusters using a machine-learning algorithm. The server system tracks user interaction with a client device during a user session and identifies the user session as belonging to a first cluster of the pluralities of clusters based at least in part on the user interaction. In accordance with identifying that the user session belongs to the first cluster, the server system personalizes content for the user session using one or more content criteria associated with the first cluster.

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Portland, OR, United States of America

Santiago Gil