Music recommendations @ MLConf 2014

transcript

April 14, 2014

Music discovery at Spotify

Music + ML = ❤

April 14, 2014

I’m Erik Bernhardsson

Engineering Manager at Spotify in NYC @fulhack

The “Prism” team

Chris Johnson Andy Sloane Sam Rozenberg Ahmad Qamar Romain Yon Gandalf Hernandez Neville Li Rodrigo Araya Edward Newett Emily Samuels Vidhya Murali Rohan Agrawal

Section name

40 million tracks... but where to start?

Discover page5

Radio6

How do you scale this?

How do we structure music understanding?

How do you teach music to machines? !Editorial tagging Audio analysis Metadata Natural language processing Collaborative filtering

Collaborative filtering

Find patterns in usage data !With millions of users and billions of streams, lots of patterns

Hey,I like tracks P, Q, R, S!

Well,I like tracks Q, R, S, T!

Then you should check out track P!

Nice! Btw try track T!

Some real data points36.5% of playlists containing Notorious BIG also contain 2Pac (6.4% of playlists containing Notorious BIG also contain Justin Bieber) !

Main problem: how similar are two items?

If you understand that well, you can do most other things. !So our main problem: how do you model a function similarity(x, y) !For item similarity it’s also much easier to acquire good test set data, unlike personal recommendations. It’s hard to evaluate personal recommendations – most offline metrics like precision are irrelevant. !

“Essentially, all models are wrong, but some are useful.”– George Box !!!We can’t perfectly model how users choose music. But modeling is a craft not a science and we can

use common sense when building models. !For play count, is Poisson or a Normal distribution better? !Always check your assumptions. Eg. SVD minimizes squared loss, which assumes the underlying

data is Gaussian. Is it?

OK so how do we do it?

There’s a lot of interesting unsupervised language models that work really well for us. Docs = playlists/users, words=tracks/artists/albums. You could also call it implicit collaborative filtering because we have no ratings whatsoever.

!Main approach: matrix factorization (or latent factor methods), historically with bag-of-words on play

counts (but today sequence is also important)

Turns out people have been doing this in NLP for a while

c11 c12 . . . c1nc21 c22 . . . c2n...

...cm1 cm2 . . . cmn

| {z }Lots of words

9>>>>>>>>=

>>>>>>>>;

Lots of documents

Or more generally:

p11 p12 . . . p1np21 p22 . . . p2n...

...pm1 pm2 . . . pmn

The idea with matrix factorization is to represent this probability distribu-tion like this:

pui = aTubi

M 0 = ATB

BBBBBB@

CCCCCCA⇡

BBBBBB@

CCCCCCA

| {z }f

� � f

BBBBBB@

. . . . . . .

CCCCCCA

| {z }probabilities for next event

BBBBBB@

CCCCCCA

| {z }user vectors

✓. . . . . . .. . . . . . .

| {z }item vectors

We can look at it as a probability distribution:0

BBBBBB@

0 0.07 0.21 00.05 0 0 0.010.04 0 0.13 0.090 0 0 0.07

0.19 0.01 0 0.130 0.03 0 0

CCCCCCA

Section name 14

Step 1: Put everything into a big sparse matrix

Using some definition of correlation. Eg. for Pearson:

Pu NuiNuj

but it’s very slow because:

BBBBBB@

0 7 21 05 0 0 14 0 13 90 0 0 719 1 0 130 3 0 0

CCCCCCA

402 19 52 28819 59 147 1352 147 610 117288 13 117 300

O(U · (N/I)2)...where U = number of users

I = number of itemsN = number of nonzero entries

⇡ 107 · (1010/107)2 = 1013 mapper outputs

It’s an extremely sparse matrix

BBBBBBBBBBBB@

......

.... . . . . . 53 . . . . . .

......

.... . . . . . . . . 12 . . .

......

.... . . 7 . . . . . . . . .

......

CCCCCCCCCCCCA

It’s a very big matrix too:

c11 c12 . . . c1nc21 c22 . . . c2n...

...cm1 cm2 . . . cmn

| {z }107 items

9>>>>>>>>>=

>>>>>>>>>;

107 users

Matrix example

Roughly 25 billion nonzero entries Total size is roughly 25 billion * 12 bytes = 300 GB (“medium data”)

Never gonna give you up

Erik listened to Never gonna give you up 1

For instance, for PLSA

Probabilistic Latent Semantic Indexing (Hoffman, 1999) Invented as a method intended for text classification

BBBBBB@

. . . . . . .

CCCCCCA⇡

BBBBBB@

CCCCCCA

| {z }user vectors

✓. . . . . . .. . . . . . .

| {z }item vectors

BBBBBB@

. . . . . . .

CCCCCCA

| {z }P (u,i)=

PzP (u|z)P (i,z)

BBBBBB@

CCCCCCA

| {z }P (u|z)

✓. . . . . . .. . . . . . .

| {z }P (i,z)

P (u|z) = 1

P (i, z) = 1

So in general we want to optimize

P (u, i)Nui =X

Nui logP (u, i) =X

Nui logX

P (u|z)P (i, z)

N logP =

BBBBBB@

0 7 21 05 0 0 14 0 13 90 0 0 719 1 0 130 3 0 0

CCCCCCAlog

BBBBBB@

CCCCCCA

✓. . . .. . . .

CCCCCCA

BBBBBB@

0 7 21 05 0 0 14 0 13 90 0 0 719 1 0 130 3 0 0

CCCCCCA

Run this for n iterations

Start with random vectors around the origin. !Then run alternating least squares, gradient descent, or something like that.

Why are latent factor models nice?

They find vectors which are super small fingerprints of the musical style or the user’s taste Usually something like 40-1000 elements

0.87 1.17 -0.26 0.56 2.21 0.77 -0.03

Latent factor 1

Latent factor 2

track x's vector

Track X:

Why are latent factor models nice? (part 2)

- Fast (linear in input size) - Do not have a big problem with overfitting - Have a solid underlying model (i.e. not just a bunch of heuristics) - Easy to scale (at least compared to other models) - Gives a compact representation of items

Similarity now becomes schoolbook trigonometry

Latent factor 1

Latent factor 2

track xtrack y

cos(x, y) = HIGH

IPMF item item:

P (i ! j) = exp(bTj bi)/Zi =

exp(bTj bi)P

k exp(bTk bi)

VECTORS:pui = aTubi

simij = cos(bi,bj) =bTi bj

|bi||bj|

i j simi,j

2pac 2pac 1.02pac Notorious B.I.G. 0.912pac Dr. Dre 0.872pac Florence + the Machine 0.26Florence + the Machine Lana Del Rey 0.81

IPMF item item MDS:

exp(� |bj � bi|2)Pk exp(� |bk � bi|2)

simij = � |bj � bi|2

(u, i, count)

IPMF item item:

exp(bTj bi)P

k exp(bTk bi)

VECTORS:pui = aTubi

simij = cos(bi,bj) =bTi bj

|bi||bj|

i j simi,j

2pac 2pac 1.02pac Notorious B.I.G. 0.912pac Dr. Dre 0.872pac Florence + the Machine 0.26Florence + the Machine Lana Del Rey 0.81

IPMF item item MDS:

exp(� |bj � bi|2)Pk exp(� |bk � bi|2)

simij = � |bj � bi|2

(u, i, count)

Why does cosine make sense?

Intuitively it makes sense, because we’re factoring out popularity and introducing a distance metric. !In fact, best result seems to be: train a latent factor model as usual, but normalize all vectors as a

post-processing step. !Even for models without any geometric interpretation (like LDA), cosine works

It’s still tricky to search for similar tracks though

Locality Sensitive Hashing: Cut the space recursively by random plane. If two points are close, they are more likely to end up on the same side of each plane. !https://github.com/spotify/annoy

Source:

…So what models have we experimented with?

Section name

Old school models

- Latent Semantic Analysis (LSA) - Probabilistic Latent Semantic Analysis (PLSA) - Latent Dirichlet Allocation (LDA) !Bag of words models Need a lot of topics, and usually not very great for music recs

What about scalability of models?

When we started experimenting with latent factor models, PLSA needed at least 400 factors (topics) to give decent results. !That gives at least 10 billion parameters, or way more that you could conveniently fit in RAM. !So what to do? We turned to Hadoop.

One iteration, one map/reduce job

“Google News Personalization: Scalable Online Collaborative Filtering”

Reduce stepMap step

u % K = 0i % L = 0

u % K = 0i % L = 1 ... u % K = 0

i % L = L-1

u % K = 1i % L = 0

u % K = 1i % L = 1 ... ...

... ... ... ...

u % K = K-1i % L = 0 ... ... u % K = K-1

i % L = L-1

item vectorsitem%L=0

item vectorsitem%L=1

item vectorsi % L = L-1

user vectorsu % K = 0

user vectorsu % K = 1

user vectorsu % K = K-1

all log entriesu % K = 1i % L = 1

u % K = 0

u % K = 1

u % K = K-1

Section name

Other MF models

- Collaborative Filtering for Implicit Feedback Datasets (“Koren”) - “vector_exp”: our own: every stream is a softmax over all tracks !Need a much more compact representation of items, typically only say 40 elements. !Benefit a lot from handling the zero case separately

Section name

New trendy models

- Recurrent neural networks (RNN) - word2vec !Take into account sequence of events !Future: Take into account the time – maybe hidden markov models, etc?

Power of combining models

All models have their own objective and their own biases. Combining them (with Gradient Boosting Decision Trees) yields kickass results:

Section name

Album cover based modelsJust a fun experiment that shows that any signal (weak learner) adds value to the ensemble. Turns

out it probably just works as a classifier for minimal techno. We will most likely never put this in production :)

What happened with Hadoop?

Most newer models don’t need a ton of latent factors, so all parameters fit nicely in RAM. !Additionally, you can do more complex things on a single machine. Lately we’ve started focusing on a combination of non-scalable models (more complex, less data) and scalable models (simple, but with more data) !Hadoop makes things “scale”, but at a ridiculous constant I/O overhead. We are in the process of moving our models to Spark instead !

Orders of magnitude numbers

Data points Parameters Time to train Single-machine model 1B 100M 10h Hadoop model 100B 10B 10h Spark?? 100B 10B 1h

Source:

What are we optimizing for?... a story of surrogate loss functions

We want to optimize Spotify’s “success”

Long term business value or something similar. Problem: You only get one shot!

Let’s run A/B tests

Typically: DAU (daily active users), Day 2 retention, etc Super inefficient way of collecting roughly 1 bit of information!

So let’s do offline testing

Editorial judgement “Look at the results” !

The “Daft Punk Test” … why does collaborative filtering always fail?

LDA RNN Koren PLSA vector_exp

Daft Punk Daft Punk Daft Punk Daft Punk Daft Punk

Daft Punk - Stardust Rizzle Kicks Coldplay The PURSUIT Gorillaz

Raccoon Daft Funk Gotye Junior Senior deadmau5

Dave Droid La Roux Lana Del Rey Chuckie & LMFAO Macklemore & Ryan Lewis

The Local Abilities Rudimental Of Monsters And Men Beatbullyz M83

Daft Funk Pacjam The Lumineers Pursuit Gotye

M83 VS Big Black Delta Su Bailey Green Day La Roux The xx

Leandro Dutra Capital Cities John Mayer Fatboy Slim Calvin Harris

Huw Costin YYZ Foster The People Chase & Drive Kavinsky

Jesús Alonso Various, WMGA Florence + The Machine Knivez Out Coldplay

Wait maybe machines can evaluate things?

Sure! We just need a ground truth data set !Use things like thumbs, skips, editorial data sets !Note that thumbs etc has observation bias !Doesn’t have to be as high volume, few thousand data points is enough !We can also optimize for this using e.g. GBDT

Again, GBDT’s are pretty cool:

Ensemble workflow

Cross validate ensemble model

Model 1 Model 2 Model 3 ... Model n

Thumbs Gradient boosted decision tree

Combined model Offline metrics

Production

Editorial data sets

This One Weird Trick Sort of Fixes Observation Bias

Augment the data set with lots of random negative data. Works well in practice.

parameter 2

parameter 1

current best estimate

data points from earlier batches

What have we learned so far?

- Figuring out what to optimize for is hard - Combining lots of models really helps - Large scale algorithms are great, but not everything has to scale

So what are we working on now?

Combine even more signals - Content-based methods: use audio, lyrics, images - Read about music and understand it - Personalize everything - Just acquired Echo Nest in Boston!

Music recommendations @ MLConf 2014

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