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Presentation about Spotify's music discovery system at MLConf NYC 2014

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- 1. April 14, 2014 Music discovery at Spotify Music + ML = ❤
- 2. April 14, 2014 I’m Erik Bernhardsson Engineering Manager at Spotify in NYC @fulhack
- 3. 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 3
- 4. Section name 40 million tracks ... but where to start? 4
- 5. Discover page 5
- 6. Radio 6
- 7. How do you scale this? 7
- 8. How do we structure music understanding? How do you teach music to machines? ! Editorial tagging Audio analysis Metadata Natural language processing Collaborative filtering 8
- 9. Collaborative filtering Find patterns in usage data ! With millions of users and billions of streams, lots of patterns 9 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!
- 10. Some real data points 36.5% of playlists containing Notorious BIG also contain 2Pac (6.4% of playlists containing Notorious BIG also contain Justin Bieber) ! 10
- 11. 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. ! 11
- 12. “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? 12
- 13. 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) 13 Or more generally: P = 0 B B B @ p11 p12 . . . p1n p21 p22 . . . p2n ... ... pm1 pm2 . . . pmn 1 C C C A The idea with matrix factorization is to represent this probability distribu- tion like this: pui = aT u bi M0 = AT B 0 B B B B B B @ 1 C C C C C C A ⇡ 0 B B B B B B @ 1 C C C C C C A | {z } f f
- 14. Section name 14
- 15. Step 1: Put everything into a big sparse matrix 15 @ . . . 7 . . . . . . . . . ... ... ... A a very big matrix too: M = 0 B B B @ c11 c12 . . . c1n c21 c22 . . . c2n ... ... cm1 cm2 . . . cmn 1 C C C A | {z } 107 items 9 >>>>>>>>>= >>>>>>>>>; 107 users
- 16. Matrix example Roughly 25 billion nonzero entries Total size is roughly 25 billion * 12 bytes = 300 GB (“medium data”) 16 Erik Never gonna give you up Erik listened to Never gonna give you up 1 times
- 17. For instance, for PLSA Probabilistic Latent Semantic Indexing (Hoffman, 1999) Invented as a method intended for text classification 17 P = 0 B B B B B B @ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 C C C C C C A ⇡ 0 B B B B B B @ . . . . . . . . . . . . 1 C C C C C C A | {z } user vectors ✓ . . . . . . . . . . . . . . ◆ | {z } item vectors PLSA 0 B B B B B B @ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 C C C C C C A | {z } P (u,i)= P z P (u|z)P (i,z) ⇡ 0 B B B B B B @ . . . . . . . . . . . . 1 C C C C C C A | {z } P (u|z) ✓ . . . . . . . . . . . . . . ◆ | {z } P (i,z) X
- 18. Run this for n iterations Start with random vectors around the origin. ! Then run alternating least squares, gradient descent, or something like that. 18
- 19. 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 19 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:
- 20. 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 20
- 21. Similarity now becomes schoolbook trigonometry 21 Latent factor 1 Latent factor 2 track x track y cos(x, y) = HIGH IPMF item item: P(i ! j) = exp(bT j bi)/Zi = exp(bT j bi) P k exp(bT k bi) VECTORS: pui = aT u bi simij = cos(bi, bj) = bT i bj |bi||bj| O(f) i j simi,j 2pac 2pac 1.0 2pac Notorious B.I.G. 0.91 2pac Dr. Dre 0.87 2pac Florence + the Machine 0.26 IPMF item item: P(i ! j) = exp(bT j bi)/Zi = exp(bT j bi) P k exp(bT k bi) VECTORS: pui = aT u bi simij = cos(bi, bj) = bT i bj |bi||bj| O(f) i j simi,j 2pac 2pac 1.0 2pac Notorious B.I.G. 0.91 2pac Dr. Dre 0.87 2pac Florence + the Machine 0.26 Florence + the Machine Lana Del Rey 0.81 IPMF item item MDS: P(i ! j) = exp(bT j bi)/Zi = exp( |bj bi| 2 ) P k exp( |bk bi| 2 )
- 22. 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 22
- 23. 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 23
- 24. Source: …So what models have we experimented with? 24
- 25. 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 25
- 26. 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. 26
- 27. One iteration, one map/reduce job “Google News Personalization: Scalable Online Collaborative Filtering” 27 Reduce stepMap step u % K = 0 i % L = 0 u % K = 0 i % L = 1 ... u % K = 0 i % L = L-1 u % K = 1 i % L = 0 u % K = 1 i % L = 1 ... ... ... ... ... ... u % K = K-1 i % L = 0 ... ... u % K = K-1 i % L = L-1 item vectors item%L=0 item vectors item%L=1 item vectors i % L = L-1 user vectors u % K = 0 user vectors u % K = 1 user vectors u % K = K-1 all log entries u % K = 1 i % L = 1 u % K = 0 u % K = 1 u % K = K-1
- 28. 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 28
- 29. 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? 29
- 30. Power of combining models All models have their own objective and their own biases. Combining them (with Gradient Boosting Decision Trees) yields kickass results: 30
- 31. Section name Album cover based models Just 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 :) 31
- 32. 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 ! 32
- 33. 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 33
- 34. Source: What are we optimizing for? ... a story of surrogate loss functions 34
- 35. We want to optimize Spotify’s “success” Long term business value or something similar. Problem: You only get one shot! 35
- 36. 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! 36
- 37. So let’s do offline testing Editorial judgement “Look at the results” ! 37
- 38. The “Daft Punk Test” … why does collaborative filtering always fail? 38 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
- 39. 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 39
- 40. Again, GBDT’s are pretty cool: 40
- 41. Ensemble workflow 41 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
- 42. This One Weird Trick Sort of Fixes Observation Bias Augment the data set with lots of random negative data. Works well in practice. 42 parameter 2 parameter 1 current best estimate + - + + + + + + + - - - -- - - data points from earlier batches
- 43. 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 43
- 44. 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! 44

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