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Large-scale recommendation, a random point of view
- 1. Anne-Marie Tousch Senior Research Scientist, Criteo AI Lab July 2d, 2019 Large-scale recommendation a random point of view @amy8492
- 2. 2 • Who am I?
- 3. Large-scale recommendation a random point of view
- 4. 4 • Retargeting User browses for products
- 5. 5 • Retargeting User browses Criteo buys the ad placement
- 6. 6 • Retargeting User browses Criteo buys the ad placement Client pays for clicks
- 8. 8 • Large scale recommendation ?
- 9. 9 • Large scale recommendation : cross-advertiser case
- 10. 10 • Large scale recommendation in a nutshell
- 11. 11 • Classical Recommendation Systems
- 12. 12 • Classical Recommendation Systems
- 13. 13 • Classical Recommendation Systems
- 14. 14 • Classical Recommendation Systems
- 15. 15 • Classical Recommendation Systems
- 17. 17 • Randomized SVD Halko et al. "Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions." SIAM review 2011
- 18. 18 • Randomized SVD Halko et al. "Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions." SIAM review 2011
- 19. 19 • Randomized SVD Halko et al. "Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions." SIAM review 2011
- 20. 20 • Randomized SVD Halko et al. "Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions." SIAM review 2011
- 22. 22 • Did you mean…
- 23. The origin
- 24. 24 • The Johnson-Lindenstrauss Lemma (1984)
- 25. 25 • log 𝑛 The Johnson-Lindenstrauss Lemma (1984) 𝜀−2
- 26. 26 • Source: https://scikit-learn.org/stable/auto_examples/plot_johnson_lindenstrauss_bound.html Johnson-Lindenstrauss
- 27. 27 • JL embeddings Source: https://scikit-learn.org/stable/auto_examples/plot_johnson_lindenstrauss_bound.html
- 28. 28 • JL embeddings Source: https://scikit-learn.org/stable/auto_examples/plot_johnson_lindenstrauss_bound.html
- 29. 29 • • Dimensionality reduction • Sketching • Approximate nearest neighbors • Random projection trees • Kernel approximations • Newton sketches for optimization • Linear programming • … Many applications Googling for sketching was the best idea :-)
- 31. 31 • Sparse random projections … Dasgupta, et al. "A sparse Johnson_Lindenstrauss transform." STOC, 2010. Achlioptas. “Database-friendly random projections: Johnson-Lindenstrauss with binary coins”. JCSS, 2003. Li et al, "Very Sparse Random Projections" , KDD 2006
- 32. 32 • Normalized Hadamard matrices: 𝐻2 = 1 2 1 1 1 −1 𝐻2𝑛 = 1 2 𝐻𝑛 𝐻𝑛 𝐻𝑛 −𝐻𝑛 HX = Fast Hadamard Transform of X. Hadamard matrices
- 33. 33 • The Fast-JLT transform = x x Ailon and Chazelle. “Approximate nearest neighbors and the fast Johnson- Lindenstrauss transform”. STOC 2006
- 34. 34 • Orthogonalize: 𝐺 = 𝑄𝑅 Rescale: 𝐺𝑂𝑅𝐹 = 1 𝜎 𝑆𝑄 with 𝑆 ∼ 𝑑𝑖𝑎𝑔(𝜒𝑑) Orthogonal random features
- 35. 35 • First used for practical spherical LSH Structured orthogonal random features 𝐺𝑆𝑂𝑅𝐹 = 𝑑 𝜎 𝐻𝐷1𝐻𝐷2𝐻𝐷3 Andoni et al. "Practical and optimal LSH for angular distance." Advances in NeurIPS. 2015. Choromanski et al. "The unreasonable effectiveness of structured random orthogonal embeddings." NeurIPS. 2017.
- 36. 36 • Define a family of hash functions 𝐹 such that: • Define a hash function ℎ from ℎ1, … , ℎ𝑘 ∈ 𝐻𝑘 , eg: ℎ 𝑥 = sgn < 𝑎, 𝑥 > with ai ∼ 𝑁(0,1) • Use 𝐿 hash tables LSH: Locality Sensitive Hashing Indyk and Motwani, “Approximate nearest neighbors: towards removing the curse of dimensionality”, STOC, 1998 Charikar, “Similarity estimation techniques from rounding algorithms”, STOC, 2002
- 37. 37 • Random Kitchen Sinks
- 38. Large scale neural networks
- 39. 39 • A dense layer Image from http://cs231n.github.io/neural-networks-1/
- 40. 40 • Adaptive Fastfood – Deep Fried Convnets Source: Yang et al, ICCV 2015
- 41. Randomizing is good for you
- 42. image source
- 44. “The only principle that does not inhibit progress is: anything goes.” Paul Feyerabend, Against Method
- 45. Next steps
Notas do Editor
- Retargeting: User browse an e-commerce website Moves on to a publisher website Criteo buys ad placements Criteo is paid if the ad is clicked
- Retargeting: User browse an e-commerce website Moves on to a publisher website Criteo buys ad placements Criteo is paid if the ad is clicked
- Retargeting: User browse an e-commerce website Moves on to a publisher website Criteo buys ad placements Criteo is paid if the ad is clicked
- For each user, and for each client, compute offline recommendations with different algorithms. Append all these « sources » with the last historical products & you have a short list of products to score online, where you can estimate probability of click with logistic regression.
- Item/User Nearest neighbors Collaborative filtering Neural networks
- One technique is to compute a vector space for products to be able to compute nearest neighbors between products
- The classical way to compute vectors is to factorize the interaction matrix, usually through a singular value decomposition (SVD). This is called collaborative filtering.
- Both dimensions may grow to infinity
- Image: https://blogs.ethz.ch/kowalski/2008/09/25/buffons-needle/
- 1. Intuition = there exists a transform with low distortion into O(log(n)) dimension (indep d!) 2. Get drawings and formulas from https://scikit-learn.org/stable/auto_examples/plot_johnson_lindenstrauss_bound.html => 3-4 slides
- The ideal setting is when n is large, and of course d > log(n)
- First idea: use a sparse projection matrix with +/- 1 Successive improvements But issues with sparse inputs Achlioptas. “Database-friendly random projections: Johnson-Lindenstrauss with binary coins”. JCSS, 2003. Li et al, "Very Sparse Random Projections" , KDD 2006 Dasgupta, et al. "A sparse Johnson_Lindenstrauss transform." STOC, 2010.
- Fourier transform idea: from uncertainty principle, if data and spectrum can’t be both sparse => work on spectrum. Randomize selection of hadamard rows. Rerandomize to ensure non-sparsity. Now P can be sparse gaussian as in original PHD, but results have been improved since and a simple coordinate sampling matrix is enough.
- Ailon and Chazelle. “Approximate nearest neighbors and the fast Johnson-Lindenstrauss transform”. STOC 2006 A step further is taken by Clarkson & Woodruff, “Low rank approximation and regression in input sparsity time”, STOC 2013, where they actually used the CountMin to sample the matrix. However, it no longer has the JL properties.
- These are not faster, but have better properties: Originally JL proof used orthogonal features, dropped by Indyk et al. for LSH Recently shown to yield lower variance kernel estimators with RFF (see later section on RFF)
- While we are currently not aware how to prove rigorously that such pseudo-random rotations perform as well as the fully random ones, empirical evaluations show that three applications of HDi are exactly equivalent to applying a true random rotation (when d tends to infinity). We note that only two applications of HDi are not sufficient.
- A well-known application to the approximate nearest neighbors problem which you might find useful in real life.
- This is a very nice application to approximating kernels. https://www.youtube.com/watch?v=Qi1Yry33TQE
- More modern applications
- 3 more examples coming
- SGD everywhere.
- Bandit algorithm & how children learn
- The exploration-exploitation trade-off exists everywhere, eg in scientific research.