Abstract: In some examples, artificial intelligence based music playlist reordering and song performance assessment may include ascertaining listening data for a plurality of tracks, and generating a plurality of sessions. Tracks that have been played more than a specified play threshold may be identified and retained. Sessions that are greater than a minimum session length threshold and less than a maximum session length threshold may be retained. Input-output track sequences may be generated for the retained sessions. Unique identifiers may be assigned to each of the tracks present across the retained sessions. Each input-output track sequence may be vectorized based on associated unique identifiers. A neural network model may be trained based on the vectorized input-output track sequences. For an input playlist, the trained neural network model may be used to generate a modified playlist. Additionally or alternatively, a user-song interaction graph may be used to generate another modified playlist.

Abstract: In some examples, artificial intelligence based music playlist curation may include ascertaining listening data for a plurality of tracks, and generating a plurality of embeddings that represent the plurality of tracks. A replacement track for an existing track in an input playlist may be generated. Alternatively or additionally, at least one additional track may be added to the input playlist. Alternatively or additionally, based on a seed set of tracks, an output playlist that includes a specified number of tracks that is greater than a number of tracks in the seed set of tracks may be generated. Alternatively or additionally, based on a plurality of specified attributes, the plurality of embeddings may be partitioned into a plurality of clusters corresponding to the plurality of specified attributes.

Abstract: In some examples, artificial intelligence based music playlist curation may include ascertaining listening data for a plurality of tracks, and generating a plurality of embeddings that represent the plurality of tracks. A replacement track for an existing track in an input playlist may be generated. Alternatively or additionally, at least one additional track may be added to the input playlist. Alternatively or additionally, based on a seed set of tracks, an output playlist that includes a specified number of tracks that is greater than a number of tracks in the seed set of tracks may be generated. Alternatively or additionally, based on a plurality of specified attributes, the plurality of embeddings may be partitioned into a plurality of clusters corresponding to the plurality of specified attributes.

Abstract: In some examples, artificial intelligence based music playlist reordering and song performance assessment may include ascertaining listening data for a plurality of tracks, and generating a plurality of sessions. Tracks that have been played more than a specified play threshold may be identified and retained. Sessions that are greater than a minimum session length threshold and less than a maximum session length threshold may be retained. Input-output track sequences may be generated for the retained sessions. Unique identifiers may be assigned to each of the tracks present across the retained sessions. Each input-output track sequence may be vectorized based on associated unique identifiers. A neural network model may be trained based on the vectorized input-output track sequences. For an input playlist, the trained neural network model may be used to generate a modified playlist. Additionally or alternatively, a user-song interaction graph may be used to generate another modified playlist.