chao liu internet services research center microsoft research-redmond
TRANSCRIPT
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Chao LiuInternet Services Research CenterMicrosoft Research-Redmond
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Motivation & Challenges Background on Distributed Computing Standard ML on MapReduce
Classification: Naïve Bayes Clustering: Nonnegative Matrix
Factorization Modeling: EM Algorithm
Customized ML on MapReduce Click Modeling Behavior Targeting
Conclusions04/21/23 2
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Data on the Web Scale: terabyte-to-petabyte data
▪ Around 20TB log data per day from Bing Dynamics: evolving data streams
▪ Click data streams with evolving/emerging topics
Applications: Non-traditional ML tasks▪ Predicting clicks & ads
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Motivation & Challenges Background on Distributed Computing Standard ML on MapReduce
Classification: Naïve Bayes Clustering: Nonnegative Matrix
Factorization Modeling: EM Algorithm
Customized ML on MapReduce Click Modeling Behavior Targeting
Conclusions04/21/23 4
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Parallel computing All processors have access to a shared
memory, which can be used to exchange information between processors
Distributed computing Each processor has its own private
memory (distributed memory), communicating over the network▪ Message passing ▪ MapReduce
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MPI is for task parallelism Suitable for CPU-intensive jobs Fine-grained communication control,
powerful computation model
MapReduce is for data parallelism Suitable for data-intensive jobs A restricted computation model
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Reducer
Aggregate values by keys
……
……Mapper
docs
(docId, doc) pairs
(w1,1)(w2,1)
(w3,1)
(w1,<1,1, 1>)
(w1, 3)
Mapper
docs
(docId, doc) pairs
(w1,1) (w3,1)
Mapper
docs
(docId, doc) pairs
(w1,1)(w2,1)
(w3,1)
Reducer
(w2,<1, 1>)
(w2, 2)
Reducer
(w3,<1,1,1>)
(w3, 3)
…
Web corpus on multiple machines
Mapper: for each word w in a doc, emit (w, 1)
Intermediate (key,value) pairs are aggregated by word
Reducer is copied to each machine to run over the intermediate data locally to produce the result
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A big picture: Not Omnipotent but good enough
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Standard ML Algorithm Customized ML Algorithm
MapReduce Friendly
• Classification: Naïve Bayes, logistic regression, MART, etc• Clustering: k-means, NMF, co-clustering, etc• Modeling: EM algorithm, Gaussian mixture, Latent Dirichlet Allocation, etc
• PageRank• Click Models• Behavior Tageting
MapReduce Unfriendly
• Classification: SVM• Clustering: Spectrum clustering
• Learning-to-Rank
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Motivation & Challenges Background on Distributed Computing Standard ML on MapReduce
Classification: Naïve Bayes Clustering: Nonnegative Matrix
Factorization Modeling: EM Algorithm
Customized ML on MapReduce Click Modeling Behavior Targeting
Conclusions04/21/23 9
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P(C|X) P(C) P(X|C) =P(C)∏P(Xj|C)
10
……
Mapper
(x(i),y(i))(j, xj
(i),y(i))
(j, xj(i),y(i))
(j, xj(i),y(i))
Reduce on y(i)
P(C)
Reduce on j
P(Xj|C)(x(i),y(i)) Mapp
er
…………
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Effective tool to uncover latent relationships in nonnegative matrices with many applications [Berry et al., 2007, Sra & Dhillon, 2006] Interpretable dimensionality reduction [Lee & Seung,
1999] Document clustering [Shahnaz et al., 2006, Xu et al, 2006]
• Challenge: Can we scale NMF to million-by-million matrices
Am
n
WH
m
nkk
0,0,0 HWA
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Am
n
WH
m
nkk
0,0,0 HWA
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Data Partition: A, W and H across machines
A…
…
),,( , jiAji
W. . . . .
),( iwi
H
. . . . .
),( jhj
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WAW
AWH
Y
XHH
T
T
*.*.
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… … …
…
),,(: , jiAjiA
),,,( , iji wAji
Map-I
Reduce-I
),( , iji wAj
Map-II
),( , iji wAj
Reduce-II
),( jxj
Map-IIIMap-IV
),0( WW T
Map-V
),0( iTi ww
…
),,,( jjj yxhj
…),( jyj
),(: iwiW ),(: jhjH
…
…
…
… ),( newjhj
Reduce-III
Reduce-V
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AWX T
… …
…
),,(: , jiAjiA
),,,( , iji wAji
Map-I
Reduce-I
),( , iji wAj
Map-II
),( , iji wAj
Reduce-II
),( jxj
),(: iwiW
…
…
…
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… …
Map-IIIMap-IV
),0( WW T
),0( iTi ww …),( jyj
),(: iwiW ),(: jhjH
Reduce-III WHWY T
m
ii
Ti
T wwWWC1
W
. . . . .
),( iwi
. . .
. . .
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…
),( jxj
Map-V
…
),,,( jjj yxhj
…),( jyj
),(: jhjH
…
… ),( newjhj
Reduce-V
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… … …
…
),,(: , jiAjiA
),,,( , iji wAji
Map-I
Reduce-I
),( , iji wAj
Map-II
),( , iji wAj
Reduce-II
),( jxj
Map-IIIMap-IV
),0( WW T
Map-V
),0( iTi ww
…
),,,( jjj yxhj
…),( jyj
),(: iwiW ),(: jhjH
…
…
…
… ),( newjhj
Reduce-III
Reduce-V
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3 hours per iteration, 20 iterations take around 20*3*0.72 ≈ 43 hours
Less than 7 hours on a 43.9M-by-769M matrix with 4.38 billion nonzero values
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Map Evaluate Compute
Reduce
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Motivation & Challenges Background on Distributed Computing Standard ML on MapReduce
Classification: Naïve Bayes Clustering: Nonnegative Matrix
Factorization Modeling: EM Algorithm
Customized ML on MapReduce Click Modeling Behavior Targeting
Conclusions04/21/23 22
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Clicks are good… Are these two
clicks equally “good”?
Non-clicks may have excuses: Not relevant Not examined
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2404/21/23
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query
URL1
URL2
URL3
URL4
C1 C2 C3 C4
S1 S2 S3 S4 Relevance
E1 E2 E3 E4Examine Snippet
ClickThroughs
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S1
E1
E2
C1
S2
C2
…
…
…
Si
Ei
Ci
the preceding click position before i
i id i r
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Ultimate goal
Observation: conditional independence
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Likelihood of search instance
From S to R:
kC
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Posterior with
Re-organize by Rj’s
How many times dj
was clicked
How many times dj was not clicked when it is at position (r + d) and the preceding click is on position r
1:nC
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Exact inference with joint posterior in closed form
Joint posterior factorizes and hence mutually independent
At most M(M+1)/2 + 1 numbers to fully characterize each posterior Count vector:
0 1 2 ( 1) 2( , , ,..., )M Me e e e e
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ComputeCount vector for
R4
r
0 0
0 0 0
0
0 1 2
d
3 2 1
0
N4
N4, r, d
1
1
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Map: emit((q,u), idx)
Reduce: construct the count vector
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(U1, 0)(U2, 4)(U3, 0)
Map
(U1, 1)(U3, 0)(U4, 7)
Map
(U1, 1)(U3, 0)(U4, 0)
Map
21 1 1( ) (1 )p R R R 2 2( ) 1 0.98p R R 3
3 3( )p R R 4 4 4( ) (1 )p R R R (U1, 0, 1, 1) (U2,
4)(U4, 0, 7)
(U3, 0, 0, 0)
Reduce
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Setup: 8 weeks data, 8
jobs Job k takes first k-
week data
• Experiment platform– SCOPE: Easy and Efficient Parallel Processing of
Massive Data Sets [Chaiken et al, VLDB’08]
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Increasing computation load more queries, more urls, more impressions
Near-constant elapse time
Computation Overload Elapse Time on SCOPE
• 3 hours• Scan 265 terabyte data• Full posteriors for 1.15 billion (query, url) pairs
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Behavior targeting Ad serving based on users’ historical
behaviors Complementary to sponsored Ads and
content Ads
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Goal Given ads in a certain category, locate qualified
users based on users’ past behaviors
Data User is identified by cookie Past behavior, profiled as a vector x, includes ad
clicks, ad views, page views, search queries, clicks, etc
Challenges: Scale: e.g., 9TB ad data with 500B entries in Aug'08 Sparse: e.g., the CTR of automotive display ads is
0.05% Dynamic: i.e., user behavior changes over time.
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CTR = ClickCnt/ViewCnt A model to predict expected click count A model to predict expected view count
Linear Poisson model
MLE on w
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Learning Map: Compute and Reduce: Update
Prediction
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Motivation & Challenges Background on Distributed Computing Standard ML on MapReduce
Classification: Naïve Bayes Clustering: Nonnegative Matrix
Factorization Modeling: EM Algorithm
Customized ML on MapReduce Click Modeling Behavior Targeting
Conclusions04/21/23 40
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Challenges imposed by Web data Scalability of standard algorithms Application-driven customized algorithms
Capability to consume huge amount of data outweighs algorithm sophistication Simple counting is no less powerful than
sophisticated algorithms when data is abundant or even infinite
MapReduce: a restricted computation model Not omnipotent but powerful enough Things we want to do turn out to be things we can
do
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Thank You!
04/21/23 SEWM‘10 Keynote, Chengdu, China 42