Smartphone Electronics Recommendation System – It’s that time of year again. On December 1st, everyone will release their “curated” playlists from music streaming app Spotify. Everyone you follow takes screenshots of their top singers and songs, and it’s like they’re providing a brief audio history of their year through social media.
This year, Spotify’s personalized classification of your 2021 soundtrack presents your most played artists, songs, genres, and even a musical mood board in a clickable interactive story. Now you can “mix” your playlist with your friend’s year of music. (Moodboards and mix playlists are new this year.)
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All of this begs the question: What’s the point of showing how users consume music? A study written last May by Spotify’s research team may have a clue. The researchers showed 10 users their Spotify account and personal data profiles based on their top songs (past month and all time), top genres, how many playlists they’ve created, and when they listen to Spotify. They found that storing a user’s personal listening history data allows them to “reflect their identity as a listener,” allowing them to see whether they only listen to music while working or have a strong obsession with a particular time. artist
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As much as we care about such a notion, we know it’s just music, and besides, we know we’re not the only fans of Taylor Swift or Lorde. Seeing a narrative (now with an emotional arc) built around the songs that shaped your years is always very personal and sometimes revealing. (If you’re okay with that, you can let an external AI judge Spotify). The thirty-eight-year-old writer once thought Spotify knew him better than he did.
So how does Spotify do it? We know they have data collected from their audience (381 million monthly active users at last count). Here’s what analysis they did behind the scenes to understand what their users like to hear.
When Spotify first launched in 2006, its goal was to be a music library. Personalization was born when app engineers realized that discovering new music they might like could enhance their experience. This can be done by providing the algorithms with information about the user’s listening history, music preference, how long certain songs have been played, and how they react to recommendations (like, skip, replay, save).
“Personalization has been a powerful experience for listeners who don’t have the time or knowledge to create endless unique playlists for every dinner party or road trip,” said Oskar Stahl, Spotify’s VP of Personalization, in an October 2021 blog post. “It opened up discoveries on a wider scale, and one person could discover hundreds of artists a year.”
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Their approach to this type of personalization relies on two main areas of research: user modeling and sophisticated audio analysis. Spotify strives to model in-app user behavior by finding ways to adapt in-app actions to a person’s feelings and emotions, and by tying the music experience to mood and situational contexts such as time of day, week or season. Knowing this will allow them to change what they recommend on Friday night and Tuesday afternoon. Recommended playlists can appear in a carousel on the home screen; There are personalized playlists like Discover Weekly, Daily Mix and Radio playlists.
Additionally, a new feature called “enhance” lets you get recommendations within a playlist you’ve already created, and this week the Spotify team is looking at an approach that uses a human editor, Stål said in a video presentation. Machine learning algorithms to create audio experiences that can mix and match songs with podcasts and more. Spotify is even testing a neural network called CoSeRNN that measures certain features, such as past listening history and current context, to offer event-specific song recommendations.
To test whether music reflects certain human characteristics, they released the results of a small survey-based study last December to see how interest in music correlates with certain personality traits. In a blog post, the researchers noted that there is some connection between personality and music genre preferences. Surprisingly, people who identified as “open to new experiences” checked out Discover Weekly more; Self-identified extroverts were more likely to listen to playlists created by other people, while those who self-identified as introverts preferred to explore the discography of a newly discovered artist.
The Spotify team seems to be constantly thinking of new ways to sort and recommend different types of music to their users. To get there, they must first take the various types of data they collect and create models that can analyze, compare, contrast, sort and group the various data they receive. The company’s researchers noted in a 2016 paper that they scan the internet for artist information and words used in online reviews to describe specific songs. They build algorithms to extract the sound structures of songs and analyze how songs are related, scanning the billions of playlists already created by users on the platform. In addition, they predict a user’s musical taste by analyzing their historical and real-time listening patterns.
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For example, Discover Weekly works by carefully analyzing the songs a user has listened to recently and scanning all playlists that might contain those songs or similar songs. Spotify uses a machine learning tool called the Nearest Neighbor Search algorithm to group songs and users based on common attributes or attributes.
“Imagine you and another person have the same four artists, but your fifth artists are different. We’ll look at those two close games and think, ‘Hmm, maybe everyone likes the other’s fifth artist,’ and offer that,” Stahl said in a blog post. “Now we’re doing this process one at a time. Imagine thousands, if not millions, of connections and preferences being reviewed instantly, not just times.”
Spotify, on the other hand, did a lot of math by splitting the song into separate instrumental layers, breaking up its rhythm and structure. In November, the music service proposed a new personalized recommendation model called MUSIG, which learns “meaningful representations of tracks and users” based on individual characteristics of songs (such as genre, acoustics, danceability, lyrics) and relatedness. published the study. . to each other (for example, they appear in the same playlist).
However, nowadays it is not enough to find what users like. Our music tastes change over time and Spotify needs to give users new music they like to keep them coming back.
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This includes recommending content that is popular or similar to music a person has listened to before, as well as discovery content that is richer and more relevant to the user’s typical activities.
“Maybe you join Spotify for dance music, but can we help you focus while studying? By balancing these poles, we can help you have a more satisfying diet,” Stål said in the video presentation. “We have to think about your needs, what keeps you in your comfort zone, and your needs, so your listening can improve, but it’s It may not be what you expect right now.”
These types of formulas “can facilitate exploration by helping users discover new content or develop new interests,” Spotify researchers wrote in a March post. “This helps the platform spread consumption across artists and makes it easier to consume less popular content.”
Of course, Spotify has its own financial reasons for wanting to diversify the tastes of its users. Internal research found that active users with diverse listening habits were “25 percent more likely to switch from free to premium than those with less diverse music consumption.”
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To stay ahead of our changing preferences and update their recommendations, Spotify needs to understand how our tastes change over time. Earlier this year, its researchers built a model based on a dataset of 100,000 Spotify users who were continuously active from 2016 to 2020. They looked at each user’s entire streaming history and grouped their music into “microgenres.” They are mapped by time. They came up with a connected graph that illustrates the transitions between different musical genres. For example, their model suggested that users could go from liking “EDM” to “nu jazz” or “gospel” to liking “tropical house,” which is cool but soulful electronic music.
By using the paths outlined in this model, Spotify hopes to gradually acclimate users to different genres by navigating the micro-genres that exist between what they already like and what they don’t yet know.
Beyond music, Spotify is a decade of personalization research