Now, ‘Personalised DJ’ music playlist algorithm matches songs to listeners’ changing moods

Researchers have developed a “DJ-like” algorithm that can create ultra-personal music playlists which adapt to listeners’ changing moods.

The algorithm called DJ-MC could outdo streaming music services by making playlists that change depending on each individual’s shifting emotion, according to the research published in the journal MIS Quarterly.

“Whether you’re getting into the car after a long day of meetings, or you’re getting out of bed on a weekend morning, it should tailor its recommendations to your changing moods,” said Maytal Saar-Tsechansky, a professor at the University of Texas at Austin in the US.

The programme runs a series of feedback loops. It tries out a song, the listener rates it, and the application heeds that rating in choosing the next song.

“Then you alter the model accordingly,” said Elad Liebman, a PhD student in computer science at UT Austin, who conceptualised the programme.

DJ-MC adapts to the listener’s mood, considering not only which songs they will enjoy, but also in what order, the researchers said.

Songs are organised intelligently, leading to an expressive,”DJ-like” sequence, instead of a random, arbitrary-sounding one, they said.

According to the researchers, DJ MC plans its moves ten songs ahead, like a chess player.

While one song is playing, the programme generates tens of thousands of possible sequences, and it predicts which one will please the listener the most, they said.

The programme serves up the next song on that playlist -- and while that song is playing, it creates and tests new sequences.

The researchers noted that in machine learning, the mechanism is known as a Monte Carlo search which inspired the name of the programme: DJ-MC.

The programme could be adapted to other kinds of media, from news stories to videos, they said.

“Learning algorithms don’t have a taste; they just have data. You can replace the dataset with anything, as long as people are consuming it similarly,” Liebman said.