Music recommendations at Spotify

Erik Bernhardsson erikbern@spotify.com

Spotify

- Launched in 2009- Available in 17 countries- 20M active users, 5M paying subscribers- Peak at 5k tracks/s, 1M logged in users- 20M tracks

Some applications

Recommendation stuff at Spotify

- Related artists:

Recommendation stuff at Spotify, cont…

More!

How can we find music?

Recommendations

- Manual classification- Feature extraction- Social media analysis, web scraping, metadata based- Collaborative filtering

Pandora & Music Genome Project

- Classifies tracks in terms of 400 attributes- Each track takes 20-30 minutes to classify- A distance function finds similar tracks

- “Subtle use of strings”- “Epic buildup”- “Acid Jazz roots”- “Beats made for dancing”- “Trippy soundscapes”- “Great trombone solo”- …

Scraping the web is another approach

Feature extraction

Collaborative filtering

Idea:- If two movies x, y get similar ratings then they are probably

similar- If a lot of users all listen to tracks x, y, z, then those tracks

are probably similar

Collaborative filtering

Get data

… lots of data

Aggregate data

Throw away temporal information and just look at the number of times

OK, so now we have a big matrix

… very big matrix

Throw out all the temporal data:

Supervised collaborative filtering is pretty much matrix completion

Supervised learning: Matrix completion

Supervised: evaluating rec quality

Unsupervised learning

- Trying to estimate the density- i.e. predict probability of future events

Try to predict the future given the past

How can we find similar items

We can calculate correlation coefficient as an item similarity

- Use something like Pearson, Jaccard, …

Amazon did this for “customers who bought this also bought”

- US patent 7113917

Parallelization is hard though

Can speed this up using various LSH tricks

- Twitter: Dimension Independent Similarity Computation (DISCO)

Are there other approaches?

Natural Language Processing has a lot of similar problems

…matrix factorization is one idea

Matrix factorization

Matrix factorization

- Want to get user vectors and item vectors- Assume f latent factors (dimensions) for each user/item

- Hofmann, 1999- Also called PLSI

Probabilistic Latent Semantic Analysis (PLSA)

PLSA, cont.

+ a bunch of constraints:

PLSA, cont.

Optimization problem: maximize log-likelihood

PLSA, cont.

“Collaborative Filtering for Implicit Feedback Datasets”

- Hu, Koren, Volinsky (2008)

“Collaborative Filtering for Implicit Feedback Datasets”, cont.

Here is another method we use

What happens each iteration

- Assign all latent vectors small random values- Perform gradient ascent to optimize log-likelihood

Calculate derivative and do gradient ascent

- Assign all latent vectors small random values- Perform gradient ascent to optimize log-likelihood

2D iteration example

Vectors are pretty nice because things are now super fast

- User-item score is a dot product:

- Item-item similarity score is a cosine similarity:

- Both cases have trivial complexity in the number of factors f:

Example: item similarity as a cosine of vectors

Two dimensional example for tracks

We can rank all tracks by the user’s vector

So how do we implement this?

Hadoop at Spotify

One iteration of a matrix factorization algorithm

“Google News personalization: scalable online collaborative filtering”

So now we solved the problem of recommendations right?

Actually what we really want is to apply it to other domains

Radio

- Artist radio: find related tracks- Optimize ensemble model based on skip/thumbs data

Learning from feedback is actually pretty hard

A/B testing

More applications!!!

Last but not least: we’re hiring!

Thank you