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Deepak-Computational Advertising-The LinkedIn Way

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Deepak-Computational Advertising-The LinkedIn Way

  1. 1. Computational Advertising: The LinkedIn Way Deepak Agarwal, LinkedIn Corporation CIKM, San Francisco Oct 30th, 2013
  2. 2. Computational Advertising  MatchMaker (Broder, CACM) – Placing the “best” ads in a given context for every user visit  Match making at scale requires automation – serving with low marginal cost increases profit margins  Automation through Machine Learning/Optimization  New discipline called Computational Advertising ©2013 LinkedIn Corporation. All Rights Reserved.
  3. 3. LinkedIn Advertising: Brand, Self-Serve, Sponsored updates
  4. 4. SERVING Ad request Profile: region = US, age = 20 Context = profile page, 300 x 250 ad slot Filter Campaigns (Targeting criteria, Frequency Cap, Budget Pacing) Automatic Format Selection Campaigns eligible for auction Serving constraint < 100 millisec Response Prediction Engine Click Cost = Bid3 x CTR3/CTR2 Sorted by Bid * CTR
  5. 5. Response Prediction: Important Input for optimization  CTR of an ad format on some slot of a LinkedIn page – E.g. CTR of 160 x 600 ad slot formats f160x600_exp_3_4 f160x600_exp_3_5 f160x600_exp_3_6 f300x250_exp_2_10  CTR of an ad on some position for a selected ad format ©2013 LinkedIn Corporation. All Rights Reserved.
  6. 6. Counting clicks and views using moving window Estimate CTR of formats for each page type Page type formats CTRpagetype,format = clickspagetypel,format/viewspagetype,format
  7. 7. Onboarding new formats, avoiding starvation Explore/exploit dilemma Format Clicks Format Format Clicks Clicks Format Clicks V1.2 1523 V1.2 1523 V1.2 1523 V1.2 1523 V2.0 34,872 V2.0 34,872 V2.0 34,872 V2.0 34,872 V2.1 37,224 V2.1 37,224 V2.1 37,224 V2.1 37,224 V3.0 0 V3.0 000 V3.0 V3.0 Views CTR % Traffic Views CTR Traffic Views CTR % %Traffic Views CTR % Traffic 624,915 0.24% ??? 624,915 25% 624,915 2.4% 2.4% 0% 624,915 2.4% 7.8% 11,839,741 0.29% ??? 11,839,741 25% 11,839,741 2.9% 2.9% 0% 11,839,741 2.9% 35.3% 12,594,481 0.30% ??? 12,594,481 25% 12,594,481 3.0% 3.0% 100% 12,594,481 3.0% 36.3% 0 0.28% ??? 000 2.8% 25% 2.8% 2.8% 20.7% 0% Explore/exploit (softmax) Explore-only (random) Exploit-only (greedy) • Maximizes & new, good & bad to be good but explore Serves old performance given current knowledge Exploit format that are known evenly • Cannot adapt to changing environment Ignores ourcould be potentially good those that knowledge about performance • Profits from current knowledge while continuing to learn
  8. 8. Evaluating Explore/Exploit schemes  We evaluated several explore/exploit techniques offline – Offline replay based on precision@1 on randomized data  Provides unbiased estimates of online performance (Langford et al, 2009) Softmax + epsilon greedy on LinkedIn advertising, provided significant gains  Thompson sampling, UCB, Softmax, epsilon-greedy with – moving window with  Different training update frequencies (few minutes, few hours, daily)  Epsilon-greedy + softmax, Thompson sampling, UCB among the promising schemes – Faster updates help with new formats initially, daily updates are fine if very few new formats introduced into the system (as in our application) – Segmenting by user attributes did not help much ©2013 LinkedIn Corporation. All Rights Reserved.
  9. 9. CTR estimates for ads: Curse of dimensionality
  10. 10. Mitigating the curse  What segments ? What are good ones ? – Too few coarse segments: fails to personalize – Too many: curse of dimensionality, data sparseness  Most segments have no clicks, 0/5 == 0/50 == 0/5M ? Pool data to mitigate sparseness 40/1000 Visits on profile page 20/20000 0/5 users from Palo Alto profile page, ad 77, user from Palo Alto Pooling with hundreds of millions of segments is challenging – Different ways to pool, we use logistic regression 10
  11. 11. Taming curse of dimensionality: Logistic Regression Dim 1 + + _ _ + + _ + _ + _ _ + _ Dim 2 _ _ _ _ -2.5 _ _ 11
  12. 12. CTR Prediction Model for Ads  Feature vectors – Member feature vector: xi – Campaign feature vector: cj – Context feature vector: zk  Model:
  13. 13. CTR Prediction Model for Ads  Feature vectors – Member feature vector: xi – Campaign feature vector: cj – Context feature vector: zk  Model: Cold-start component Warm-start per-campaign component
  14. 14. CTR Prediction Model for Ads  Feature vectors – Member feature vector: xi – Campaign feature vector: cj – Context feature vector: zk Cold-start: Warm-start:  Model: Both can have L2 penalties. Cold-start component Warm-start per-campaign component
  15. 15. Model Fitting  Single machine (well understood) – – – – conjugate gradient L-BFGS Trusted region …  Model Training with Large scale data – Cold-start component Θw is more stable  Weekly/bi-weekly training good enough  However: difficulty from need for large-scale logistic regression – Warm-start per-campaign model Θc is more dynamic  New items can get generated any time  Big loss if opportunities missed  Need to update the warm-start component as frequently as possible
  16. 16. Model Fitting  Single machine (well understood) – – – – conjugate gradient L-BFGS Trusted region …  Model Training with Large scale data – Cold-start component Θw is more stable Large Scale Logistic Regression  Weekly/bi-weekly training good enough  However: difficulty from need for large-scale logistic regression – Warm-start per-campaign model Θc is more dynamic  New items can get generated any time  Big loss if opportunities missed  Need to update the warm-start component as frequently as possible Per-item logistic regression given Θc
  17. 17. Large Scale Logistic Regression: Computational Challenge     Hundreds of millions/billions of observations Hundreds of thousands/millions of covariates Fitting a logistic regression model on a single machine not feasible Model fitting iterative using methods like gradient descent, Newton’s method etc – Multiple passes over the data      Problem: Find x to min(F(x)) Iteration n: xn = xn-1 – bn-1 F’(xn-1) bn-1 is the step size that can change every iteration Iterate until convergence Conjugate gradient, LBFGS, Newton trust region, …
  18. 18. Compute using Map-Reduce Big Data Partition 1 Partition 2 … Partition N Mapper 1 Mapper 2 … Mapper N <Key, Value> <Key, Value> <Key, Value> <Key, Value> Reducer 1 Reducer 2 … Reducer M Output 1 Output 1 Output 1 Output 1
  19. 19. Large Scale Logistic Regression  Naïve: – Partition the data and run logistic regression for each partition – Take the mean of the learned coefficients – Problem: Not guaranteed to converge to global solution  Alternating Direction Method of Multipliers (ADMM) – – – – Boyd et al. 2011 Set up constraints: each partition’s coefficient = global consensus Solve the optimization problem using Lagrange Multipliers Advantage: converges to global solution
  20. 20. Large Scale Logistic Regression via ADMM Iteration 1 BIG DATA Partition 1 Partition 2 Partition 3 Partition K Logistic Regression Logistic Regression Logistic Regression Logistic Regression Consensus Computation
  21. 21. Large Scale Logistic Regression via ADMM Iteration 1 BIG DATA Partition 1 Partition 2 Partition 3 Partition K Logistic Regression Logistic Regression Logistic Regression Logistic Regression Consensus Computation
  22. 22. Large Scale Logistic Regression via ADMM Iteration 2 BIG DATA Partition 1 Partition 2 Partition 3 Partition K Logistic Regression Logistic Regression Logistic Regression Logistic Regression Consensus Computation
  23. 23. Large Scale Logistic Regression via ADMM  Notation – – – – (Xi , yi): data in the ith partition βi: coefficient vector for partition i β: Consensus coefficient vector r(β): penalty component such as ||β||22  Optimization problem
  24. 24. ADMM updates LOCAL REGRESSIONS Shrinkage towards current best global estimate UPDATED CONSENSUS
  25. 25. ADMM at LinkedIn  Lessons and Improvements – Initialization is important (ADMM-M)  Use the mean of the partitions’ coefficients  Reduces number of iterations by 50% – Adaptive step size (learning rate) (ADMM-MA)  Exponential decay of learning rate – Together, these optimizations reduce training time from 10h to 2h
  26. 26. Explore/Exploit with Logistic Regression E/E: Sample a line from the posterior (Thompson Sampling) _ COLD START _ COLD + WARM START for an Ad-id + + + + _ + _ + _ _ + _ _ _ _ _ _ _ POSTERIOR of WARM-START COEFFICIENTS 26
  27. 27. Models Considered  CONTROL: per-campaign CTR counting model  COLD-ONLY: only cold-start component  LASER: our model (cold-start + warm-start)  LASER-EE: our model with Explore-Exploit using Thompson sampling
  28. 28. Metrics  Model metrics – Test Log-likelihood – AUC/ROC – Observed/Expected ratio  Business metrics (Online A/B Test) – CTR – CPM (Revenue per impression)
  29. 29. Observed / Expected Ratio  Observed: #Clicks in the data  Expected: Sum of predicted CTR for all impressions  Not a “standard” classifier metric, but in many ways more useful for this application  What we usually see: Observed / Expected < 1 – Quantifies the “winner’s curse” aka selection bias in auctions  When choosing from among thousands of candidates, an item with mistakenly over-estimated CTR may end up winning the auction  Particularly helpful in spotting inefficiencies by segment – E.g. by bid, number of impressions in training (warmness), geo, etc. – Allows us to see where the model might be giving too much weight to the wrong campaigns  High correlation between O/E ratio and model performance online
  30. 30. Offline: ROC Curves 1.0 ● ● 0.8 ● ● ● 0.6 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.4 ● ● ● 0.2 ● 1.0 0.4 False Positive Rate 0.8 ● ● ● 0.2 0.0 CONTROL [ 0.672 ] COLD−ONLY [ 0.757 ] LASER [ 0.778 ] 0.6 ● 0.0 True Positive Rate ● ● ● ● ●
  31. 31. Online A/B Test  Three models – CONTROL (10%) – LASER (85%) – LASER-EE (5%)  Segmented Analysis – 8 segments by campaign warmness  Degree of warmness: the number of training samples available in the training data for the campaign  Segment #1: Campaigns with almost no data in training  Segment #8: Campaigns that are served most heavily in the previous batches so that their CTR estimate can be quite accurate
  32. 32. Daily CTR Lift Over Control +% LASER LASER−EE ● +% ● +% ● ● +% ● Day 7 Day 6 Day 5 Day 4 Day 3 ● Day 2 +% ● Day 1 Percentage of CTR Lift ●
  33. 33. Daily CPM Lift Over Control +% ● LASER LASER−EE +% +% ● ● +% ● Day 7 Day 5 Day 3 Day 2 ● Day 6 ● +% Day 4 +% Day 1 Percentage of eCPM Lift ● ●
  34. 34. CPM Lift By Campaign Warmness Segments Lift Percentage of CPM +% +% 0% −% −% LASER LASER−EE −% 1 2 3 4 5 6 Campaign Warmness Segment 7 8
  35. 35. O/E Ratio By Campaign Warmness Segments Observed Click/Expected Clicks 1 0.9 0.8 0.7 0.6 CONTROL LASER LASER−EE 0.5 1 2 3 4 5 6 Campaign Warmness Segment 7 8
  36. 36. Number of Campaigns Served Improvement from E/E
  37. 37. Insights  Overall performance: – LASER and LASER-EE are both much better than control – LASER and LASER-EE performance are very similar  Segmented analysis by campaign warmness – Segment #1 (very cold)  LASER-EE much worse than LASER due to its exploration property  LASER much better than CONTROL due to cold-start features – Segments #3 - #5  LASER-EE significantly better than LASER  Winner’s curse hit LASER – Segment #6 - #8 (very warm)  LASER-EE and LASER are equivalent  Number of campaigns served – LASER-EE serves significantly more campaigns than LASER – Provides healthier market place
  38. 38. Theory vs. Practice Textbook Reality  Data is stationary  Training data is clean  Training is hard, testing and inference are easy  Models don’t change  Complex algorithms work best  Features, items changing constantly  Fraud, bugs,tracking delays, online/offline inconsistencies, etc.  All aspects have challenges at web scale  Never-ending processes of improvement  Simple models with good features and lots of data win
  39. 39. Solutions to Practical Problems  Rapid model development cycle – Quick reaction to changes in data, product – Write once for training, testing, inference  Can adapt to changing data – Integrated Thompson sampling explore/exploit – Automatic training – Multiple training frequencies for different parts of model  Good tools yield good models – Reusable components for feature extraction and transformation – Very high-performance inference engine for deployment – Modelers can concentrate on building models, not re-writing common functions or worrying about production issues
  40. 40. Summary  Reducing dimension through logistic regression coupled with explore/exploit schemes like Thompson sampling effective mechanism to solve response prediction problems in advertising  Partitioning model components by cold-start (stable) and warm-start (non-stationary) with different training frequencies effective mechanism to scale computations  ADMM with few modifications effective model training strategy for large data with high dimensionality  Methods work well for LinkedIn advertising, significant improvements ©2013 LinkedIn Corporation. All Rights Reserved.
  41. 41. Collaborators  I won’t be here without them, extremely lucky to work with such talented individuals Liang Zhang Jonathan Traupman Romer Rosales Bo Long Doris Xin
  42. 42. Current Work  Investigating Spark and various other fitting algorithms – Promising results, ADMM still looks good on our datasets  Stream Ads – Multi-response prediction (clicks, shares, likes, comments) – Filtering low quality ads extremely important  Revenue/Engagement tradeoffs (Pareto optimal solutions)  Stream Recommendation – Holistic solution to both content and ads on the stream  Large scale ML infrastructure at LinkedIn – Powers several recommendation systems ©2013 LinkedIn Corporation. All Rights Reserved.
  43. 43. We are hiring !  Interns for summer 2014 (contact me dagarwal@linkedin.com)  Full-time – Graduating PhDs, experienced researchers ©2013 LinkedIn Corporation. All Rights Reserved.
  44. 44. Backup slides ©2013 LinkedIn Corporation. All Rights Reserved.
  45. 45. LASER Configuration  Feature processing pipeline – Sources: transform external data into feature vectors – Transformers: modify/combine feature vectors – Assembler: Packages features vectors for training/inference  Configuration language – – – – Model structure can be changed extensively Library of reusable components Train, test, and deploy models without any code changes Speeds up model development cycle
  46. 46. LASER Transformer Pipeline Request User profile Item Context Source User Source Item Source Subset Subset Interaction Assembler Training or Inference
  47. 47. LASER Performance  Real time inference – About 10µs per inference (1500 ads = 15ms) – Reacts to changing features immediately  “Better wrong than late” – If a feature isn’t immediately available, back off to prior value  Asynchronous computation – Actions that block or take time run in background threads  Lazy evaluation – Sources & transformers do not create feature vectors for all items – Feature vectors are constructed/transformed only when needed  Partial results cache – Logistic regression inference is a series of dot products – Scalars are small; cache can be huge – Hardware-like implementation to minimize locking and heap pressure

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