mahout and recommendations
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1©MapR Technologies 2013- Confidential
Introduction to MahoutAnd How To Build a Recommender
2©MapR Technologies 2013- Confidential
Me, Us
Ted Dunning, Chief Application Architect, MapRCommitter PMC member, Mahout, Zookeeper, DrillBought the beer at the first HUG
MapRDistributes more open source components for HadoopAdds major technology for performance, HA, industry standard API’s
TonightHash tag - #dfwbd #maprSee also - @ApacheMahout @ApacheDrill
@ted_dunning and @mapR
3©MapR Technologies 2013- Confidential
Requested Topic For Tonight
What is Mahout? What makes it different? How can big data technology solve impossible problems? How is big data affecting the world?
4©MapR Technologies 2013- Confidential
Also
What is MapR? What is MapR doing? How does MapR’s technology work? How are customers making use of MapR? How can anyone make use of MapR to solve problems?
5©MapR Technologies 2013- Confidential
Oh … Also This
Detailed break-down of a live machine learning system running with Mahout on MapR
With code examples
6©MapR Technologies 2013- Confidential
I may have to summarize
7©MapR Technologies 2013- Confidential
I may have to summarize
just a bit
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Part 1:5 minutes of math
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Part 2:12 minutes: I want a pony
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Part 3:A working example
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What Does Machine Learning Look Like?
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What Does Machine Learning Look Like?
O(κ k d + k3 d) = O(k2 d log n + k3 d) for small k, high qualityO(κ d log k) or O(d log κ log k) for larger k, looser quality
But tonight we’re going to show you how to keep it simple yet powerful…
13©MapR Technologies 2013- Confidential
Comparison of Three Main ML Topics
Recommendation: – Involves observation of interactions between people taking action (users)
and items for input data to the recommender model– Goal is to suggest additional appropriate or desirable interactions– Applications include: movie, music or map-based restaurant choices;
suggesting sale items for e-stores or via cash-register receipts
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Part 1:A bit of math
(the math of bits)
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Mahout Math
Goals are– basic linear algebra,– and statistical sampling,– and good clustering,– decent speed,– extensibility,– especially for sparse data
But not – totally badass speed– comprehensive set of algorithms– optimization, root finders, quadrature
18©MapR Technologies 2013- Confidential
Matrices and Vectors
At the core:– DenseVector, RandomAccessSparseVector– DenseMatrix, SparseRowMatrix
Highly composable API
Important ideas: – view*, assign and aggregate– iteration
m.viewDiagonal().assign(v)
19©MapR Technologies 2013- Confidential
Assign? View?
Why assign?– Copying is the major cost for naïve matrix packages– In-place operations critical to reasonable performance– Many kinds of updates required, so functional style very helpful
Why view?– In-place operations often required for blocks, rows, columns or diagonals– With views, we need #assign + #views methods– Without views, we need #assign x #views methods
Synergies– With both views and assign, many loops become single line
24©MapR Technologies 2013- Confidential
Examples
double alpha; a.assign(alpha);
a.assign(b, Functions.chain( Functions.plus(beta), Functions.times(alpha));
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More Examples
The trace of a matrix
Set diagonal to zero
Set diagonal to negative of row sums
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Examples
The trace of a matrix
Set diagonal to zero
Set diagonal to negative of row sums
m.viewDiagonal().zSum()
28©MapR Technologies 2013- Confidential
Examples
The trace of a matrix
Set diagonal to zero
Set diagonal to negative of row sums
m.viewDiagonal().zSum()
m.viewDiagonal().assign(0)
29©MapR Technologies 2013- Confidential
Examples
The trace of a matrix
Set diagonal to zero
Set diagonal to negative of row sums excluding the diagonal
m.viewDiagonal().zSum()
m.viewDiagonal().assign(0)
Vector diag = m.viewDiagonal().assign(0);diag.assign(m.rowSums().assign(Functions.MINUS));
32©MapR Technologies 2013- Confidential
Clustering and Such
Streaming k-means and ball k-means– streaming reduces very large data to a cluster sketch– ball k-means is a high quality k-means implementation– the cluster sketch is also usable for other applications– single machine threaded and map-reduce versions available
SVD and friends– stochastic SVD has in-memory, single machine out-of-core and map-reduce
versions– good for reducing very large sparse matrices to tall skinny dense ones
Spectral clustering– based on SVD, allows massive dimensional clustering
33©MapR Technologies 2013- Confidential
Mahout Math Summary
Matrices, Vectors– views– in-place assignment– aggregations– iterations
Functions– lots built-in– cooperate with sparse vector optimizations
Sampling– abstract samplers– samplers as functions
Other stuff … clustering, SVD
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Part 2:How recommenders work
(I still want a pony)
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Recommendations
Behavior of a crowd helps us understand what individuals will do
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Recommendations
Alice got an apple and a puppy
Charles got a bicycle
Alice
Charles
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Recommendations
Alice got an apple and a puppy
Charles got a bicycle
Bob got an apple
Alice
Bob
Charles
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Recommendations
What else would Bob like??
Alice
Bob
Charles
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Recommendations
What if everybody gets a pony?
Now what does Bob want??
Alice
Bob
Charles
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Log Files
Alice
Bob
Charles
Alice
Bob
Charles
Alice
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Log Files
u1
u3
u2
u1
u3
u2
u1
t1
t2
t3
t4
t3
t3
t1
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Log Files and Dimensions
u1
u3
u2
u1
u3
u2
u1
t1
t2
t3
t4
t3
t3
t1
t1
t2
t3
t4
Things u1 Alice
BobCharles
u3u2
Users
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History Matrix
Alice
Bob
Charles
✔ ✔ ✔
✔ ✔
✔ ✔
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Cooccurrence Matrix
1 2
1 1
1
1
2 1
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Indicator Matrix
✔
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Indicator Matrix
✔
id: t4title: puppydesc: The sweetest little puppy ever.keywords: puppy, dog, pet
indicators: (t1)
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Problems with Raw Cooccurrence
Very popular items co-occur with everything– Welcome document– Elevator music
That isn’t interesting– We want anomalous cooccurrence
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Recommendation Basics
Coocurrence
t3 not t3
t1 2 1
not t1 1 1
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Spot the Anomaly
Root LLR is roughly like standard deviations
A not A
B 13 1000
not B 1000 100,000
A not A
B 1 0
not B 0 2
A not A
B 1 0
not B 0 10,000
A not A
B 10 0
not B 0 100,000
0.44 0.98
2.26 7.15
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A Quick Simplification
Users who do h (a vector of things a user has done)
Also do r
User-centric recommendations(transpose translates back to things)
Item-centric recommendations(change the order of operations)
A translates things into users
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Symmetry Gives Cross Recommentations
Conventional recommendations with off-line learning
Cross recommendations
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For example
Users enter queries (A)– (actor = user, item=query)
Users view videos (B)– (actor = user, item=video)
ATA gives query recommendation– “did you mean to ask for”
BTB gives video recommendation– “you might like these videos”
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The punch-line
BTA recommends videos in response to a query– (isn’t that a search engine?)– (not quite, it doesn’t look at content or meta-data)
54©MapR Technologies 2013- Confidential
Real-life example
Query: “Paco de Lucia” Conventional meta-data search results:– “hombres del paco” times 400– not much else
Recommendation based search:– Flamenco guitar and dancers– Spanish and classical guitar– Van Halen doing a classical/flamenco riff
55©MapR Technologies 2013- Confidential
Real-life example
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Hypothetical Example
Want a navigational ontology? Just put labels on a web page with traffic– This gives A = users x label clicks
Remember viewing history– This gives B = users x items
Cross recommend– B’A = label to item mapping
After several users click, results are whatever users think they should be
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Nice. But we can do better?
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users
things
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users
thingtype 1
thingtype 2
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Part 3:What about that worked example?
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http://bit.ly/18vbbaT
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SolRIndexerSolR
IndexerSolrindexing
Cooccurrence(Mahout)
Item meta-data
Indexshards
Complete history
Analyze with Map-Reduce
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SolRIndexerSolR
IndexerSolrsearchWeb tier
Item meta-data
Indexshards
User history
Deploy with Conventional Search System
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Objective Results
At a very large credit card company
History is all transactions
Development time to minimal viable product about 4 months
General release 2-3 months later
Search-based recs at or equal in quality to other techniques
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Summary
Input: Multiple kinds of behavior on one set of things
Output: Recommendations for one kind of behavior with a different set of things
Cross recommendation is a special case
67©MapR Technologies 2013- Confidential
Objective Results
At a very large credit card company
History is all transactions
Development time to minimal viable product about 4 months
General release 2-3 months later
Search-based recs at or equal in quality to other techniques
68©MapR Technologies 2013- Confidential
Me, Us
Ted Dunning, Chief Application Architect, MapRCommitter PMC member, Mahout, Zookeeper, DrillBought the beer at the first HUGtdunning@{apache.org,maprtech.com} ted.dunning@gmail.com
MapRDistributes more open source components for HadoopAdds major technology for performance, HA, industry standard API’s
TonightHash tag - #dfwbd #maprSee also - @ApacheMahout @ApacheDrill
@ted_dunning and @mapR
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