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Simple Matrix Factorization for Recommendation in Apache Mahout

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Data Science London
Data Science London

This document discusses matrix factorization techniques for recommendation systems. It explains that user-item interaction data can be represented as a matrix and decomposed into two lower-rank matrices that capture latent features. One matrix represents users and the other represents items. The document outlines an alternating least squares algorithm to compute the decomposed matrices and discusses how the technique can be implemented in Apache Mahout and Myrrix for scalable recommendations.

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6 42 8 78 14 98 1 7 8 Simple Matrix Factorization for Recommendation Sean Owen • Apache Mahout
Apache Mahout • Scalable machine learning • (Mostly) Hadoop-based • Clustering, classification and recommender engines • Nearest-neighbor • User-based mahout.apache.org • Item-based • Slope-one • Clustering-based • Latent factor • SVD-based • ALS • More!
Matrix = Associations  Things are associated Rose Navy Olive Like people to colors Alice 0 +4 0  Associations have strengths Like preferences and dislikes Bob 0 0 +2  Can quantify associations Alice loves navy = +4, Carol -1 0 -2 Carol dislikes olive = -2 Dave +3 0 0  We don’t know all associations Many implicit zeroes
From One Matrix, Two  Like numbers, matrices can n be factored  m•n matrix = m•k times k•n  Associations can m P = decompose into others k n  Alice likes navy = • Alice loves blues, and k Y’ blues includes navy m X
In Terms of Few Features  Can explain associations by appealing to underlying intermediate features (e.g. “blue-ness”)  Relatively few (one “blue-ness”, but many shades) (Blue) (Alice) (Navy)
Losing Information is Helpful  When k (= features) is small, information is lost  Factorization is approximate (Alice appears to like blue-ish periwinkle too) (Blue) (Alice) (Periwinkle) (Navy)
How to Compute? n k n • k Y’ = m P m X
Skip the Singular Value Decomposition for now … n k n • Σ •k T’ = m A m S
Alternating Least Squares  Collaborative Filtering for Implicit Feedback Datasets www2.research.att.com/~yifanhu/PUB/cf.pdf  R = matrix of user-item interactions “strengths”  P = R reduced to 0 and 1  Factor as approximate P ≈ X•Y’  Start with random Y  Compute X such that X•Y’ best approximates P (Frobenius / L2 norm) (Least Squares)  Repeat for Y (Alternating)  Iterate, Iterate, Iterate  Large values in X•Y’ are good recommendations
Example 1 4 3 1 1 1 0 0 3 0 0 1 0 0 4 3 2 0 1 0 1 1 R P 5 2 3 1 0 1 0 1 5 0 0 0 1 0 2 4 1 1 0 0 0
k = 3, λ=2, α=40 1 iteration 1 1 1 0 0 2.18 -0.01 0.35 0.43 0.48 0.48 0.16 0.10 0 0 1 0 0 1.83 -0.11 -0.68 -0.27 0.39 -0.13 0.03 0.05 ≈ 0 1 0 1 1 0.79 1.15 -1.80 -0.03 -0.09 -0.13 -0.47 -0.47 1 0 1 0 1 0.97 -1.90 -2.12 Y’ 0 0 0 1 0 1.01 -0.25 -1.77 1 1 0 0 0 2.33 -8.00 1.06 X
k = 3, λ=2, α=40 1 iteration 1 1 1 0 0 0.94 1.00 1.00 0.18 0.07 0 0 1 0 0 0.84 0.89 0.99 0.60 0.50 ≈ 0 1 0 1 1 0.07 0.99 0.46 1.01 0.98 X•Y’ 1 0 1 0 1 1.00 -0.09 1.00 1.08 0.99 0 0 0 1 0 0.55 0.54 0.75 0.98 0.92 1 1 0 0 0 1.01 0.99 0.98 -0.13 -0.25
k = 3, λ=2, α=40 10 iterations 1 1 1 0 0 0.96 0.99 0.99 0.38 0.93 0 0 1 0 0 0.44 0.39 0.98 -0.11 0.39 ≈ 0 1 0 1 1 0.70 0.99 0.42 0.98 0.98 X•Y’ 1 0 1 0 1 1.00 1.04 0.99 0.44 0.98 0 0 0 1 0 0.11 0.51 -0.13 1.00 0.57 1 1 0 0 0 0.97 1.00 0.68 0.47 0.91
Interesting Because… This is all very parallelizable by row, column
BONUS: Folding in New Data  Model building takes time  Apply some right inverse: ⌃ X•Y’•(Y’)-1 = Q•(Y’)-1 = so  Sometimes need X = Q•(Y’)-1 immediate, if approximate, updates for new data  OK, what is (Y’)-1?  For new user U, need new  Of course (Y’•Y)•(Y’•Y)-1 = I row, XU•Y’ = QU, but have PU  So Y’•(Y•(Y’•Y)-1) = I and  What is XU? right inverse is Y•(Y’•Y)-1  Xu = QU•Y•(Y’•Y)-1 and so Xu ≈ Pu•Y•(Y’•Y)-1
In Mahout  org.apache.mahout.cf.  MAHOUT-737 taste.hadoop.als. ParallelALSFactorizationJob  Alternate implementation  Alternating least squares of alternating least squares  Distributed, Hadoop- based  And more…  org.apache.mahout.cf.  DistributedLanczosSolver taste.impl.recommender.  SequentialOutOfCoreSvd svd.SVDRecommender  …  SVD-based  Non-distributed, not Hadoop
 Complete product  Real-time Serving Layer Myrrix  Hadoop-based Computation Layer  Tuned, documented  Free / open: Serving Layer, for small data  Commercial: add Computation Layer for big data; Hosting  Matrix factorization-based, attractive properties  http://myrrix.com
Thank You srowen at myrrix.com mahout.apache.org

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Simple Matrix Factorization for Recommendation in Apache Mahout

  • 1. 6 42 8 78 14 98 1 7 8 Simple Matrix Factorization for Recommendation Sean Owen • Apache Mahout
  • 2. Apache Mahout • Scalable machine learning • (Mostly) Hadoop-based • Clustering, classification and recommender engines • Nearest-neighbor • User-based mahout.apache.org • Item-based • Slope-one • Clustering-based • Latent factor • SVD-based • ALS • More!
  • 3. Matrix = Associations  Things are associated Rose Navy Olive Like people to colors Alice 0 +4 0  Associations have strengths Like preferences and dislikes Bob 0 0 +2  Can quantify associations Alice loves navy = +4, Carol -1 0 -2 Carol dislikes olive = -2 Dave +3 0 0  We don’t know all associations Many implicit zeroes
  • 4. From One Matrix, Two  Like numbers, matrices can n be factored  m•n matrix = m•k times k•n  Associations can m P = decompose into others k n  Alice likes navy = • Alice loves blues, and k Y’ blues includes navy m X
  • 5. In Terms of Few Features  Can explain associations by appealing to underlying intermediate features (e.g. “blue-ness”)  Relatively few (one “blue-ness”, but many shades) (Blue) (Alice) (Navy)
  • 6. Losing Information is Helpful  When k (= features) is small, information is lost  Factorization is approximate (Alice appears to like blue-ish periwinkle too) (Blue) (Alice) (Periwinkle) (Navy)
  • 7. How to Compute? n k n • k Y’ = m P m X
  • 8. Skip the Singular Value Decomposition for now … n k n • Σ •k T’ = m A m S
  • 9. Alternating Least Squares  Collaborative Filtering for Implicit Feedback Datasets www2.research.att.com/~yifanhu/PUB/cf.pdf  R = matrix of user-item interactions “strengths”  P = R reduced to 0 and 1  Factor as approximate P ≈ X•Y’  Start with random Y  Compute X such that X•Y’ best approximates P (Frobenius / L2 norm) (Least Squares)  Repeat for Y (Alternating)  Iterate, Iterate, Iterate  Large values in X•Y’ are good recommendations
  • 10. Example 1 4 3 1 1 1 0 0 3 0 0 1 0 0 4 3 2 0 1 0 1 1 R P 5 2 3 1 0 1 0 1 5 0 0 0 1 0 2 4 1 1 0 0 0
  • 11. k = 3, λ=2, α=40 1 iteration 1 1 1 0 0 2.18 -0.01 0.35 0.43 0.48 0.48 0.16 0.10 0 0 1 0 0 1.83 -0.11 -0.68 -0.27 0.39 -0.13 0.03 0.05 ≈ 0 1 0 1 1 0.79 1.15 -1.80 -0.03 -0.09 -0.13 -0.47 -0.47 1 0 1 0 1 0.97 -1.90 -2.12 Y’ 0 0 0 1 0 1.01 -0.25 -1.77 1 1 0 0 0 2.33 -8.00 1.06 X
  • 12. k = 3, λ=2, α=40 1 iteration 1 1 1 0 0 0.94 1.00 1.00 0.18 0.07 0 0 1 0 0 0.84 0.89 0.99 0.60 0.50 ≈ 0 1 0 1 1 0.07 0.99 0.46 1.01 0.98 X•Y’ 1 0 1 0 1 1.00 -0.09 1.00 1.08 0.99 0 0 0 1 0 0.55 0.54 0.75 0.98 0.92 1 1 0 0 0 1.01 0.99 0.98 -0.13 -0.25
  • 13. k = 3, λ=2, α=40 10 iterations 1 1 1 0 0 0.96 0.99 0.99 0.38 0.93 0 0 1 0 0 0.44 0.39 0.98 -0.11 0.39 ≈ 0 1 0 1 1 0.70 0.99 0.42 0.98 0.98 X•Y’ 1 0 1 0 1 1.00 1.04 0.99 0.44 0.98 0 0 0 1 0 0.11 0.51 -0.13 1.00 0.57 1 1 0 0 0 0.97 1.00 0.68 0.47 0.91
  • 14. Interesting Because… This is all very parallelizable by row, column
  • 15. BONUS: Folding in New Data  Model building takes time  Apply some right inverse: ⌃ X•Y’•(Y’)-1 = Q•(Y’)-1 = so  Sometimes need X = Q•(Y’)-1 immediate, if approximate, updates for new data  OK, what is (Y’)-1?  For new user U, need new  Of course (Y’•Y)•(Y’•Y)-1 = I row, XU•Y’ = QU, but have PU  So Y’•(Y•(Y’•Y)-1) = I and  What is XU? right inverse is Y•(Y’•Y)-1  Xu = QU•Y•(Y’•Y)-1 and so Xu ≈ Pu•Y•(Y’•Y)-1
  • 16. In Mahout  org.apache.mahout.cf.  MAHOUT-737 taste.hadoop.als. ParallelALSFactorizationJob  Alternate implementation  Alternating least squares of alternating least squares  Distributed, Hadoop- based  And more…  org.apache.mahout.cf.  DistributedLanczosSolver taste.impl.recommender.  SequentialOutOfCoreSvd svd.SVDRecommender  …  SVD-based  Non-distributed, not Hadoop
  • 17.  Complete product  Real-time Serving Layer Myrrix  Hadoop-based Computation Layer  Tuned, documented  Free / open: Serving Layer, for small data  Commercial: add Computation Layer for big data; Hosting  Matrix factorization-based, attractive properties  http://myrrix.com
  • 18. Thank You srowen at myrrix.com mahout.apache.org