Spotify Recommendation Algorithm

Spotify is the world's largest music streaming service, with a staggering 500 million monthly users, including 205 million subscribers in more than 180 markets []. It is renowned for its personalized playlists created with the help of its advanced recommendation algorithm. While they weren't the first to use analytics to recommend music, their success lies in using a combination of computational techniques to make the recommendations feel more lifelike and realistic.

When the early 2000s rolled around, many people started to use streaming platforms such as Pandora and Last FM to discover new music. In 2008, Spotify changed the game to music discovery by introducing machine learning technology to create personalized user experiences. Discover Weekly, a feature on Spotify, uses a scalable factor analysis to compare and match user-generated playlists from over two billion users to the current listening behavior of each user. Through this, Spotify generated more accurate music recommendations for its users than the top charts of rival streaming platforms.

By leveraging AI, Spotify can analyze and tailor content to each user to provide them with a more enjoyable experience. This could include customized playlists, tailored artist recommendations, and more. In short, AI allows Spotify to personalize its platform for each user, making their music streaming experience more enjoyable.

Here is how Spotify uses AI to personalize user experiences on the platform.

Collaborative filtering

Collaborative filtering is a process that examines the collective data to identify patterns in how tracks are grouped into playlists. It is a way to construct a map of music and podcasts. Analyzing the data can determine which tracks often occur together and in which playlists. This helps to create a more comprehensive overview of the music or podcast landscape.

This map is generated using collaborative filtering, which looks at how users have put tracks into playlists and listened to them. Each point on the map represents a different track in the Spotify catalogue, and the location of each point is determined by how often these tracks have been put together in playlists. Consequently, songs that have been frequently placed together in playlists will be close together on the map, while songs that have never been put together will be further apart.

Recommendations based solely on collaborative filtering can be far from perfect, as seen in the example of Mariah Carey's "All I Want for Christmas Is You" being recommended alongside "Silent Night" during the holiday season. Even though the two songs are vastly different in terms of genre and the fact that one is a classic Christmas carol, proximity-based recommendations might still suggest "Silent Night" to users who enjoy Mariah Carey's music, even though they are not interested in Christmas carols.

Content-based filtering

In order to avoid inaccurate music recommendations, Spotify implements an additional process known as content-based filtering. This algorithm collects the metadata of songs, such as their genre and the year of release. Then it carries out an audio analysis to create a list of metrics, like "danceability," "energy", and "loudness," which describe the track's sonic qualities. By combining the data from the metadata and audio analysis, Spotify can provide more accurate music recommendations to its users.

For example, the song Dynamite by BTS has an impressive danceability score of 0.746 on a scale of 0 to 1 and an energy score of 0.765.

  "acousticness": 0.0112,
  "analysis_url": "",
  "danceability": 0.746,
  "duration_ms": 199054,
  "energy": 0.765,
  "id": "5QDLhrAOJJdNAmCTJ8xMyW",
  "instrumentalness": 0,
  "key": 6,
  "liveness": 0.0936,
  "loudness": -4.41,
  "mode": 0,
  "speechiness": 0.0993,
  "tempo": 114.044,
  "time_signature": 4,
  "track_href": "",
  "type": "audio_features",
  "uri": "spotify:track:5QDLhrAOJJdNAmCTJ8xMyW",
  "valence": 0.737

On the other hand, Fly Me To The Moon by Frank Sinatra has a lower danceability score of 0.591 and a much lower energy score of 0.298.

  "acousticness": 0.549,
  "analysis_url": "",
  "danceability": 0.591,
  "duration_ms": 147520,
  "energy": 0.298,
  "id": "7FXj7Qg3YorUxdrzvrcY25",
  "instrumentalness": 0.00000222,
  "key": 4,
  "liveness": 0.0797,
  "loudness": -12.269,
  "mode": 0,
  "speechiness": 0.06,
  "tempo": 119.954,
  "time_signature": 4,
  "track_href": "",
  "type": "audio_features",
  "uri": "spotify:track:7FXj7Qg3YorUxdrzvrcY25",
  "valence": 0.372

This starkly contrasts the prior song, illustrating the difference in danceability and energy between two very different musical artists.

Spotify's content-based filtering also considers the cultural context of the tracks, which involves analyzing the lyrics and the adjectives used to describe them. This type of filtering provides a more comprehensive understanding of the song and helps Spotify to recommend more relevant and accurate music to its users.

Two potential problems or limitations

Firstly, the recommendation biases. The potential risk associated with algorithms is that they can amplify pre-existing biases and prejudices. This could mean that if a specific music catalogue has a higher proportion of female artists than male artists, these disparities will become even more pronounced as listeners interact with the collection of tracks. This leads to a cyclical effect where these gender biases are continually reinforced and magnified.

Secondly, the cold start problem. The cold start problem is a significant issue for algorithms regarding optimizing for new artists, as there needs to be user data available to use when making recommendations. To combat this, human editors are employed to fill in the gap, as they can understand new releases, culture and what people find relevant. In other words, human editors are essential when it comes to delivering suggestions that consider the latest trends.

Learn more

If you are interested in learning more about music personalization at Spotify, then you should consider reading about this paper. It provides valuable insight into how Spotify is personalizing music for its users. Additionally, it discusses the techniques used to create more tailored experiences for music fans, as well as how they are leveraging data to drive their decisions.