Embed Size (px)
Citation preview
October 17, 2015
Data Pipelines for Music Recommendations
@ Spotify
Vidhya Murali@vid052
Vidhya Murali
Who Am I?
2
•Areas of Interest: Data & Machine Learning•Data Engineer @Spotify•Masters Student from the University of Wisconsin Madison
aka Happy Badger for life!
“Torture the data, and it will confess!”
3
– Ronald Coase, Nobel Prize Laureate
Spotify’s Big Data
4
•Started in 2006, now available in 58 countries• 70+ million active users, 20+ million paid subscribers• 30+ million songs in our catalog, ~20K added every day• 1.5 billion playlists so far and counting• 1 TB of user data logged every day• Hadoop cluster with 1500 nodes • ~20,000 Hadoop jobs per day
Music Recommendations at Spotify
Features:DiscoverDiscover WeeklyMomentsRadioRelated Artists
5
6
30 million tracks…What to recommend?
Approaches 7
•Manual curation by Experts
•Editorial Tagging
•Metadata (e.g. Label provided data, NLP over News, Blogs)
•Audio Signals
•Collaborative Filtering Model
Approaches 7
•Manual curation by Experts
•Editorial Tagging
•Metadata (e.g. Label provided data, NLP over News, Blogs)
•Audio Signals
•Collaborative Filtering Model
Collaborative Filtering Model 8
•Find patterns from user’s past behavior to generate recommendations
•Domain independent
•Scalable
•Accuracy (Collaborative Model) >= Accuracy (Content Based Model)
Definition of CF
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!
Legacy Slide of Erik Bernhardsson
The YoLo Problem 10
The YoLo Problem 10
•YoLo Problem: “You Only Listen Once” to judge recommendations•Goal: Predict if users will listen to new music (new to user)
The YoLo Problem 10
•YoLo Problem: “You Only Listen Once” to judge recommendations•Goal: Predict if users will listen to new music (new to user)
•Challenges• Scale of catalog (30M songs + ~20K added every day)• Repeated consumption of music is not very uncommon• Music is niche• Music consumption is heavily influenced by user’s lifestyle
The YoLo Problem 10
•YoLo Problem: “You Only Listen Once” to judge recommendations•Goal: Predict if users will listen to new music (new to user)
•Challenges• Scale of catalog (30M songs + ~20K added every day)• Repeated consumption of music is not very uncommon• Music is niche• Music consumption is heavily influenced by user’s lifestyle
• Input: Feedback is implicit through streaming behavior, collection adds, browse history, search history etc
User Plays to Track Recs 11
User Plays to Track Recs 11
1. Weighted play counts from logs
User Plays to Track Recs 11
1. Weighted play counts from logs
2. Train Model using the input signals
User Plays to Track Recs 11
1. Weighted play counts from logs
2. Train Model using the input signals
3. Generate recs from the trained model
User Plays to Track Recs 11
1. Weighted play counts from logs
2. Train Model using the input signals
3. Generate recs from the trained model
4. Post process the recommendations
12
Step 1: ETL of Logs
•Extract and transform the anonymized logs to training data set•Case: Logs -> (user, track, wt.count)
Step 2: Construct Big Matrix! 13
Tracks(n)
Users(m)
Vidhya
Burn by Ellie Goulding
Step 2: Construct Big Matrix! 13
Tracks(n)
Users(m)
Vidhya
Burn by Ellie Goulding
Order of 70M x 30M!
Latent Factor Models 14
Vidhya Burn
.. . . . .
.. . . . .
.. . . . .
.. . . . .
.. . . . .
•Use a “small” representation for each user and items(tracks): f-dimensional vectors
.. .
.. .
.. .
.. . . .
.. .
.. .
.. .
.. .
. .m m
n
m n
Latent Factor Models 14
Vidhya Burn
.. . . . .
.. . . . .
.. . . . .
.. . . . .
.. . . . .
•Use a “small” representation for each user and items(tracks): f-dimensional vectors
.. .
.. .
.. .
.. . . .
.. .
.. .
.. .
.. .
. .m m
n
m n
User Track Matrix: (m x n)
Latent Factor Models 14
Vidhya Burn
.. . . . .
.. . . . .
.. . . . .
.. . . . .
.. . . . .
•Use a “small” representation for each user and items(tracks): f-dimensional vectors
.. .
.. .
.. .
.. . . .
.. .
.. .
.. .
.. .
. .m m
n
m n
User Vector Matrix: X: (m x f)
User Track Matrix: (m x n)
Latent Factor Models 14
Vidhya Burn
.. . . . .
.. . . . .
.. . . . .
.. . . . .
.. . . . .
•Use a “small” representation for each user and items(tracks): f-dimensional vectors
.. .
.. .
.. .
.. . . .
.. .
.. .
.. .
.. .
. .m m
n
m n
User Vector Matrix: X: (m x f)
Track Vector Matrix: Y: (n x f)
User Track Matrix: (m x n)
Latent Factor Models 14
Vidhya Burn
.. . . . .
.. . . . .
.. . . . .
.. . . . .
.. . . . .
•Use a “small” representation for each user and items(tracks): f-dimensional vectors
.. .
.. .
.. .
.. . . .
.. .
.. .
.. .
.. .
. .
(here, f = 2)
m m
n
m n
User Vector Matrix: X: (m x f)
Track Vector Matrix: Y: (n x f)
User Track Matrix: (m x n)
Matrix Factorization using Implicit Feedback 15
Matrix Factorization using Implicit Feedback
User Track Play Count Matrix
15
Matrix Factorization using Implicit Feedback
User Track Play Count Matrix
User Track Preference
Matrix
Binary Label: 1 => played 0 => not played
15
Matrix Factorization using Implicit Feedback
User Track Play Count Matrix
User Track Preference
Matrix
Binary Label: 1 => played 0 => not played
Weights Matrix
Weights based on play count and smoothing
15
Equation(s) Alert!16
Implicit Matrix Factorization 17
1 0 0 0 1 0 0 10 0 1 0 0 1 0 0 1 0 1 0 0 0 1 10 1 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1
•Aggregate all (user, track) streams into a large matrix•Goal: Approximate binary preference matrix by the inner product of 2 smaller matrices by
minimizing the weighted RMSE (root mean squared error) using a function of total plays as weight •Why?: Once learned, the top recommendations for a user are the top inner products between
their latent factor vector in X and the track latent factor vectors in Y.
X YUsers
Tracks
• = bias for user• = bias for item• = regularization parameter
• = 1 if user streamed track else 0• • = user latent factor vector• = item latent factor vectoryi
Alternating Least Squares 18
1 0 0 0 1 0 0 10 0 1 0 0 1 0 0 1 0 1 0 0 0 1 10 1 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1
X YUsers
Tracks
• = bias for user• = bias for item• = regularization parameter
• = 1 if user streamed track else 0• • = user latent factor vector• = item latent factor vector
Fix tracks
•Aggregate all (user, track) streams into a large matrix•Goal: Approximate binary preference matrix by the inner product of 2 smaller matrices by
minimizing the weighted RMSE (root mean squared error) using a function of total plays as weight •Why?: Once learned, the top recommendations for a user are the top inner products between
their latent factor vector in X and the track latent factor vectors in Y.
yi
19
1 0 0 0 1 0 0 10 0 1 0 0 1 0 0 1 0 1 0 0 0 1 10 1 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1
X YUsers
• = bias for user• = bias for item• = regularization parameter
• = 1 if user streamed track else 0• • = user latent factor vector• = item latent factor vector
Fix tracks
Solve for users
•Aggregate all (user, track) streams into a large matrix•Goal: Approximate binary preference matrix by the inner product of 2 smaller matrices by
minimizing the weighted RMSE (root mean squared error) using a function of total plays as weight •Why?: Once learned, the top recommendations for a user are the top inner products between
their latent factor vector in X and the track latent factor vectors in Y.
Alternating Least Squares
yi
Tracks
20
1 0 0 0 1 0 0 10 0 1 0 0 1 0 0 1 0 1 0 0 0 1 10 1 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1
X YUsers
• = bias for user• = bias for item• = regularization parameter
• = 1 if user streamed track else 0• • = user latent factor vector• = item latent factor vector
Fix users
•Aggregate all (user, track) streams into a large matrix•Goal: Approximate binary preference matrix by the inner product of 2 smaller matrices by
minimizing the weighted RMSE (root mean squared error) using a function of total plays as weight •Why?: Once learned, the top recommendations for a user are the top inner products between
their latent factor vector in X and the track latent factor vectors in Y.
Alternating Least Squares
yi
Tracks
21
1 0 0 0 1 0 0 10 0 1 0 0 1 0 0 1 0 1 0 0 0 1 10 1 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1
X YUsers
• = bias for user• = bias for item• = regularization parameter
• = 1 if user streamed track else 0• • = user latent factor vector• = item latent factor vector
Fix usersSolve for tracks
•Aggregate all (user, track) streams into a large matrix•Goal: Approximate binary preference matrix by the inner product of 2 smaller matrices by
minimizing the weighted RMSE (root mean squared error) using a function of total plays as weight •Why?: Once learned, the top recommendations for a user are the top inner products between
their latent factor vector in X and the track latent factor vectors in Y.
Alternating Least Squares
yi
Tracks
22
1 0 0 0 1 0 0 10 0 1 0 0 1 0 0 1 0 1 0 0 0 1 10 1 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1
X YUsers
• = bias for user• = bias for item• = regularization parameter
• = 1 if user streamed track else 0• • = user latent factor vector• = item latent factor vector
Fix usersSolve for tracks
Repeat until convergence…
•Aggregate all (user, track) streams into a large matrix•Goal: Approximate binary preference matrix by the inner product of 2 smaller matrices by
minimizing the weighted RMSE (root mean squared error) using a function of total plays as weight •Why?: Once learned, the top recommendations for a user are the top inner products between
their latent factor vector in X and the track latent factor vectors in Y.
Alternating Least Squares
yi
Tracks
23
1 0 0 0 1 0 0 10 0 1 0 0 1 0 0 1 0 1 0 0 0 1 10 1 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1
X YUsers
• = bias for user• = bias for item• = regularization parameter
• = 1 if user streamed track else 0• • = user latent factor vector• = item latent factor vector
Fix usersSolve for tracks
Repeat until convergence…
•Aggregate all (user, track) streams into a large matrix•Goal: Approximate binary preference matrix by the inner product of 2 smaller matrices by
minimizing the weighted RMSE (root mean squared error) using a function of total plays as weight •Why?: Once learned, the top recommendations for a user are the top inner products between
their latent factor vector in X and the track latent factor vectors in Y.
Alternating Least Squares
yi
Tracks
Vectors•“Compact” representation for users and items(tracks) in the same space
Why Vectors? 25
•Vectors encode higher order dependencies
•Users and Items in the same vector space!•Use vector similarity to compute:•Item-Item similarities•User-Item recommendations
•Linear complexity: order of number of latent factors
•Easy to scale up
26
• Compute track similarities and track recommendations for users as a similarity measure
Step 3: Compute Recs!
• Euclidian Distance
• Cosine Similarity
• Pearson Correlation
26
• Compute track similarities and track recommendations for users as a similarity measure
Step 3: Compute Recs!
Recommendations via Cosine Similarity 27
Recommendations via Cosine Similarity 27
28
Annoy
• 70 million users, at least 4 million tracks for candidates per user• Brute Force Approach: • O(70M x 4M x 10) ~= 0(3 peta-operations)!
• Approximate Nearest Neighbor Oh Yeah!
• Uses Local Sensitive Hashing
• Clone: https://github.com/spotify/annoy
29
• Apply Filters• Interacted music• Holiday music anyone?
• Factor for:• Diversity• Freshness• Popularity• Demographics• Seasonality
Step 4: Post Processing
30
70 Million users x 30 Million tracks. How to
scale?
Matrix Factorization with MapReduce 31
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
•Split the matrix up into K x L blocks.•Each mapper gets a different block, sums up intermediate terms, then key by
user (or item) to reduce final user (or item) vector.
Matrix Factorization with MapReduce 32
One map taskDistributed
cache:All user vectors where u % K = x
Distributed cache:
All item vectors where i % L = y
Mapper Emit contributions
Map input:tuples (u, i, count)
where u % K = x
andi % L = y
Reducer New vector!
• Input to Mapper is a list of (user, item, count) tuples– user modulo K is the same for all users in block– item modulo L is the same for all items in the block– Mapper aggregates intermediate contributions for each user (or item)
– Eg: K=4, Mapper #1 gets user 1, 5, 9, 13 etc– Reducer keys by user (or item), aggregates intermediate mapper sums and solves closed form for final user
(or item) vector
Music Recommendations Data Flow
33
34
Source:
Revisiting YOLO!35
“You Only Listen Once to judge recommendations” problem
Optimizing for the Yolo Problem
•OFFLINE TESTING: •Experts’ Inputs •Measure accuracy
•A/B TESTS: control vs a/b group. Some useful metrics we consider: •DAU / WAU / MAU•Retention•Session Length•Skip Rate
36
Challenge Accepted!
•Cold start problem for both users and new music/upcoming artists: •Content based signals, real time recommendation
•Measuring recommendation quality:• A/B test metrics•Active forums for getting user feedback
•Scam Attacks:•Rule based model to detect scammers
•Humans choices are not always predictable: •Faith in humanity
37
What Next?
•Personalize user experience on Spotify for every moment: •Right Now
•Recommend other media formats:•Podcasts•Video
•Power music recommendations on other platforms:•Google Now
38
Join the Band!
We are hiring!
39
