Music recommendations at Spotify Erik
Bernhardsson erikbern@spotify.com

Recommendation stuff at Spotify

Collaborative filtering

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

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

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

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

Probabilistic Latent Semantic Analysis (PLSA)
- Hofmann, 1999 - Also called PLSI

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.

“Collaborative Filtering for Implicit Feedback
Datasets”, cont.

“Collaborative Filtering for Implicit Feedback
Datasets”, cont.

“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

What happens each iteration -
Assign all latent vectors small random values - Perform gradient ascent to optimize log-likelihood

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?

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

A/B testing

A/B testing

A/B testing

More applications!!!

Last but not least: we’re
hiring!

Thank you