Launching a radical change to a habitual feature, used by millions every day, presents an interesting challenge.
Categories for Human-Computer Interaction
Recommender systems typically look to users' past consumption to predict what they may want next. In practice, this approach tends to work best when what the user wants is similar to what they have consumed recently, and when it is relatively easy for that person to evaluate new items.
TastePaths: Enabling deeper exploration and understanding of personal preferences in recommender systemsMarch 18, 2022 9:30 am
Existing recommender systems are limited in the ability to help us grow and understand our personal music preferences Recommender systems... View Article
Quality user feedback is important for making good music recommendations. Recommending the right music to a user at the right... View Article
“Silent” data shapes our listening experiences To drive personalization and help listeners discover music, Spotify and other music streaming services... View Article