Seattle Scalability Mahout

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Talk given at the Seattle Scalability / NoSQL / Hadoop / etc MeetUp on March 31, 2010

Talk given at the Seattle Scalability / NoSQL / Hadoop / etc MeetUp on March 31, 2010

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  • And the usual references for LSI and Spectral Decomposition


  • 1. Numerical Recipes
    Jake Mannix
    Principal SDE, LinkedIn
    Committer, Apache Mahout, Zoie, Bobo-Browse, Decomposer
    Author, Lucene in Depth (Manning MM/DD/2010)
  • 2. A Mathematician’s Apology
    What mathematical structure describes all of these?
    Full-text search:
    Score documents matching “query string”
    Collaborative filtering recommendation:
    Users who liked {those} also liked {these}
    (Social/web)-graph proximity:
    People/pages “close” to {this} are {these}
  • 3. Matrix Multiplication!
  • 4. Full-text Search
    Vector Space Model of IR
    Corpus as term-document matrix
    Query as bag-of-words vector
    Full-text search is just:
  • 5. Collaborative Filtering
    User preference matrix
    (and item-item similarity matrix )
    Input user as vector of preferences
    (simple) Item-based CF recommendations are:
  • 6. Graph Proximity
    Adjacency matrix:
    2nd degree adjacency matrix:
    Input all of a user’s “friends” or page links:
    (weighted) distance measure of 1st – 3rd degree connections is then:
  • 7. Dictionary
    Applications Linear Algebra
  • 8. How does this help?
    In Search:
    Latent Semantic Indexing (LSI)
    probabalistic LSI
    Latent Dirichlet Allocation
    In Recommenders:
    Singular Value Decomposition
    Layered Restricted Boltzmann Machines
    (Deep Belief Networks)
    In Graphs:
    Spectral Decomposition / Spectral Clustering
  • 9. Often use “Dimensional Reduction”
    To alleviate the sparse Big Data problem of “the curse of dimensionality”
    Used to improve recall and relevance
    in general: smooth the metric on your data set
  • 10. New applications with Matrices
    If Search is finding doc-vector by:
    and users query with data represented: Q =
    Giving implicit feedback based on click-through per session: C =
  • 11. … continued
    Then has the form (docs-by-terms) for search!
    Approach has been used by Ted Dunning at Veoh
    (and probably others)
  • 12. Linear Algebra performance tricks
    Naïve item-based recommendations:
    Calculate item similarity matrix:
    Calculate item recs:
    Express in one step:
    In matrix notation:
    Re-writing as:
    is the vector of preferences for user “v”,
    is the vector of preferences of item “i”
    The result is the matrix sum of the outer (tensor) products of these vectors, scaled by the entry they intersect at.
  • 13. Item Recommender via Hadoop
  • 14. Apache Mahout
    Apache Mahout currently on release 0.3
    Will be a “Top Level Project” soon (before 0.4)
    ( )
    “Scalable Machine Learning with commercially friendly licensing”
  • 15. Mahout Features
    absorbed the Taste project
    Classification (Naïve Bayes, C-Bayes, more)
    Clustering (Canopy, fuzzy-K-means, Dirichlet, etc…)
    Fast non-distributed linear mathematics
    absorbed the classic CERN Colt project
    Distributed Matrices and decomposition
    absorbed the Decomposer project
    mahout shell-script analogous to $HADOOP_HOME/bin/hadoop
    $MAHOUT_HOME/bin/mahout kmeans –i “in” –o “out” –k 100
    $MAHOUT_HOME/bin/mahout svd –i “in” –o “out” –k 300
    Taste web-app for real-time recommendations
  • 16. DistributedRowMatrix
    Wrapper around a SequenceFile<IntWritable,VectorWritable>
    Distributed methods like:
    Matrix transpose();
    Matrix times(Matrix other);
    Vector times(Vectorv);
    Vector timesSquared(Vectorv);
    To get SVD: pass into DistributedLanczosSolver:
    LanczosSolver.solve(Matrix input, Matrix eigenVectors, List<Double> eigenValues, int rank);
  • 17. Questions?
  • 18. Appendix
    There are lots of ways to deal with sparse Big Data, and many (not all) need to deal with the dimensionality of the feature-space growing beyond reasonable limits, and techniques to deal with this depend heavily on your data…
    That having been said, there are some general techniques
  • 19. Dealing with Curse of Dimensionality
    Sparseness means fast, but overlap is too small
    Can we reduce the dimensionality (from “all possible text tokens” or “all userIds”) while keeping the nice aspects of the search problem?
    If possible, collapse “similar” vectors (synonymous terms, userIds with high overlap, etc…) towards each other while keeping “dissimilar” vectors far apart…
  • 20. Solution A: Matrix decomposition
    Singular Value Decomposition (truncated)
    “best” approximation to your matrix
    Used in Latent Semantic Indexing (LSI)
    For graphs: spectral decomposition
    Collaborative filtering (Netflix leaderboard)
    Issues: very computation intensive
    no parallelized open-source packages see Apache Mahout
    Makes things too dense
  • 21. SVD: continued
    Hadoopimpl. in Mahout (Lanczos)
    O(N*d*k) for rank-k SVD on N docs, delt’s each
    Density can be dealt with by doing Canopy Clustering offline
    But only extracting linear feature mixes
    Also, still very computation intensive and I/O intensive (k-passes over data set), are there better dimensional reduction methods?
  • 22. Solution B: Stochastic Decomposition co-ocurrence-based kernel + online Random Projection + SVD
  • 23. Co-ocurrence-based kernel
    Extract bigram phrases / pairs of items rated by the same person (using Log-Likelihood Ratio test to pick the best)
    “Disney on Ice was Amazing!” -> {“disney”, “disney on ice”, “ice”, “was” “amazing”}
    {item1:4, item2:5, item5:3, item9:1} -> {item1:4, (items1+2):4.5, item2:5, item5:3,…}
    Dim(features) goes from 105to 108+(yikes!)
  • 24. Online Random Projection
    Randomly project kernelized text vectors down to “merely” 103dimensions with a Gaussian matrix
    Or project eachnGram down to an random (but sparse) 103-dim vector:
    V= {123876244 =>1.3} (tf-IDF of “disney”)
    V’= c*{h(i) => 1, h(h(i)) =>1, h(h(h(i))) =>1}
    (c= 1.3 / sqrt(3))
  • 25. Outer-product and Sum
    Take the 103-dim projected vectors and outer-product with themselves,
    result is 103x103-dim matrix
    • sum these in a Combiner
    All results go to single Reducer, where you compute…
  • 26. SVD
    SVD-them quickly (they fit in memory)
    Over and over again (as new data comes in)
    Use the most recent SVD to project your (already randomly projected) text still further (now encoding “semantic” similarity).
    SVD-projected vectors can be assigned immediately to nearest clusters if desired
  • 27. References
    Randomized matrix decomposition review:
    Sparse hashing/projection:
    John Langford et al. “VowpalWabbit”