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Semelhante a Deep Dive to Learning to Rank for Graph Search.pptx
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Deep Dive to Learning to Rank for Graph Search.pptx
- 1. Learning To Rank For Graph Search Junfeng He, Search Quality and Ranking Joint work with Cristina, Rajat, Hieu, Allan, Maxime, Jiayan, Ethan, Scott, Kittpat, Alessandro, etc. 10/21/2013
- 2. Motivation of This Talk â Halloween! The most scary Halloween gift: You have 10 seconds to leave this room. If you choose to stay, you are fully responsible for all possible consequences.
- 3. Outline Ranking model for graph search Learn the ranking model for graph search Experiments and Discussions
- 4. Browse Quereies â photo vertical example Photos of Rajat, Photos by my friends Photos liked by Cristina, Photos commented on by me Photos in Hawaii, photos before 2010, recent photos, ⊠Photos of my friends in Hawaii this year My recommended photos of Girish Kumar's co-workers taken in United States this year liked by Tom Stocky that are commented on by friends of Lars Eilstrup Rasmussen
- 5. The Concept of Ranking Model -- typeahead f1: FEATURE_SHARED_MUTUAL_FRIEN f2: FEATURE_DISTANCE_GRAPH f1: 30 f2: 1 f1: 8 f2: 2 score: 0.86 score: 0.4 A Toy User Scoring Model with Two Features
- 6. Ranking Model -- Browse FEATURE_BROWSE_PHOTO_OF_USER:1 FEATURE_CONSTRAINTS_RATIO: 1 FEATURE_PHOTO_AGE_IN_DAYS_INV: 0.0196 FEATURE_PHOTO_COEFFICIENT_MAX: 0.2817745947658 FEATURE_PHOTO_HAS_FACE:1 âŠ.. Score: 0.96 FEATURE_BROWSE_PHOTO_OF_USER:1 FEATURE_CONSTRAINTS_RATIO: 1 FEATURE_PHOTO_AGE_IN_DAYS_INV: 0.0268 FEATURE_PHOTO_COEFFICIENT_MAX: 2.2E-308 FEATURE_PHOTO_HAS_FACE:1 âŠ.. Score: 0.82
- 7. Ranking Model Intuitively, for searcher aâs request b, what should be the score for result/document c? âą score: a real number. âą Results with larger score s should be ranked higher Mathematically, score = Ranking_model(feature_vector) feature_vector: features that contain info about searcher, query, result. Ranking_model: which maps a set of features (i.e., a feature vector) to a score.
- 8. Ranking Features Total number of features for all verticals today: ~1200 Each vertical contains a subset of features Photo browse: about 100 features. Examples: âą FEATURE_BROWSE_PHOTO_IN: whether the query is âphotos in some placeâ âą FEATURE_PHOTO_HAS_FACE: whether this photo has face âą FEATURE_PHOTO_NUM_FRIENDS_LIKED: how many of searcherâs friends liked this photo ⊠⹠In sum, features contain info about searcher, query, result(document)
- 9. Bucket Ranker For continuous features: piecewise linear f1 s f2 s For discrete features: step-wise f1 s Can approximate any nonlinear functions if we create enough number of buckets One bucket: the region between two borders One bucket: Every border is one bucket
- 10. Bucket Ranker One example: { "features" : {"FEATURE_SHARED_MUTUAL_FRIENDS" : [0,1,11,240]}, "weights" : [ 0.0206305, 0.0555313, 0.284588, 0]}, f1 Suppose x is the value of one data on feature FEATURE_SHARED_MUTUAL_FRIENDS If x <= 0, score= 0.0206305 If x == 1, score= 0.0206305 + 0.0555313 If 1<= x <=11, score= 0.0206305 + 0.0555313+ 0.284588 *(x-1)/(11-1) If 11<= x <=240, score= 0.0206305 + 0.0555313+ 0.284588+ 0*(x- 11)/(240-11) If x > 240, score= 0.0206305 + 0.0555313+ 0.284588+ 0 s1
- 11. Bucket Ranker Output of the scoring model: the sum of score from bucket each on each feature f1 f 1 ï„ ï„ ï ï ïœ ïœ Features j j j Features j j f BR s f R ) ( ) ( f2 f 2 s1 s2 f1: 30 ï s1: 0.36 f1: 8 ï s1: 0.3 f2: 1 ï s2: 0.5 f2: 2 ï s2: 0.1 score: 0.86 score: 0.4
- 12. Conditioned Bucket ranker, i.e., ranking tree Condition 1: âphoto of userâ Condition 2: âphoto in âŠâ f1 s f2 s Feature 1 Feature 2 Feature 1 Feature 2 ï„ ï„ ï ï ïœ ïœ Conditons i Features j j ij f BR f R s ) ( ) ( f1 s f2 s Face feature Face feature Outdoor feature Outdoor feature For different query intent: Face features is very important positive feature for âphoto of userâ query, but is not important for âphotos in some placeâ.
- 13. ï„ ï„ ï ï ïœ ïœ Conditons i Features j j ij f BR f R s ) ( ) ( Example: Condition 1: âphoto of userâ Condition 2: âphoto in some placeâ For one query âphotos of user in some placeâ, it will get score from buckets under both condition 1 and 2 Condition 1: âphoto of userâ Condition 2: âphoto in âŠâ f1 s f2 s Feature 1 Feature 2 Feature 1 Feature 2 f1 s f2 s Face feature Face feature Outdoor feature Outdoor feature photos of my friends photos taken in Hawaii photos of my friends taken in Hawaii
- 14. A Defense for the Bucket Ranker Model Question: Why Bucket Ranker, why not Linear, Random Forests, Boosted Decision Trees, LambdaMART, Bayesian Graph Model (yet)? Answer: A White Box model: Good interpretation/debug ability We still have lots of problems on features, labeling, data logging, etc., so our data may not be ready to train a black box model We often need to manually modify the model (e.g., support new queries, hot fix for an important bug, cover corner cases when training data is not good enough, etc.,) Complex enough to guarantee good ranking quality
- 15. A Defense for the Bucket Ranker Model Bucket Ranker gives us a good tradeoff between interpretation/debug ability and ranking quality interpretation ability linear Bucket Ranker Black Box Models Ranking Quality (up to now) linear Bucket Ranker Black Box Models What if black box models is significantly better? A brilliant idea: use the score from black box model as label to train a white box model
- 16. Engineers in our search ranking team used to manually tune the model, sometimes consisting of hundreds of curves (i.e., piecewise linear functions) âą Tedious! âą Unproductive! Donât know what the weights/curves should be Machine learning to rescue !
- 17. Outline Ranking model for graph search Learn the ranking model for graph search Training Data The Workflow to Learn Conditioned Bucket Ranker Learning To Rank Techniques Experiments and Discussions
- 18. Training Data Results shown to users Random results/samples from the same search session, but not shown to users, collected in indexing servers
- 19. Basic Labeling All random samples are labeled as negative data -1 Results with target actions, (e.g., click, friending, etc.) are labeled as positive data +1 Results without target actions, are labeled as negative data -1
- 20. More Labeling Strategies typeahead_balanced: ignore negative results under the positive results positive_only: ignore all negative results, only use negative samples âŠâŠ Basic labeling is usually the best, or good enough
- 21. Outline Ranking model for graph search Learn the ranking model for graph search Training Data The Workflow to Learn Conditioned Bucket Ranker Learning To Rank Techniques Example Results on photo search
- 22. How to Choose the Conditions and Features Conditions: Manually chosen up to now, incorporate human domain knowledge Features: usually need to remove obvious meaningless features like Doc_id Some ongoing tasks about suggesting condition and feature automatically e.g., frequent pattern mining on queries Condition 1: âphoto of userâ Condition 2: âphoto in âŠâ Feature 1 Feature 2 Feature 1 Feature 2
- 23. Create Bucket Borders For continuers features or discrete features with many possible values âą Percentile âą make sure each buckets contains the same number of data For discrete features with few possible values (like binary features), or category features such as user locale âą each feature value is one bucket
- 24. Create Bucket Borders Discrete features with skew distribution: Iterative percentile histogram
- 25. Learn the Bucket Ranker Condition 1: âphoto of userâ Condition 2: âphoto in âŠâ f1 s f2 s Feature 1 Feature 2 Feature 1 Feature 2 f1 s f2 s We have condition, features, and bucket borders now, the only thing to learn is the weights of each bucket.
- 26. Feature transformation --Bucketization ] 0 ,... 0 , 8 . 0 , 1 ,... 1 [ : 'ij f 23 : ij f : ij f the feature vector for feature j under condition i, after bucketization. A vector with the dimension == number of buckets ij f' the feature value for feature j under condition i, a scalar Scale invariant with percentile buckets
- 27. Feature transformation -- Linearization ïŸ ïœïŒ ï ïœ ïœ ï„ ï„ ï ï x w f h f ij ij , ' ' ) ( Conditons i Features j R s ] 0 ,...,... 0 , ' , 0 ,... 0 [ : ij f x Condition 1: âphoto of userâ Condition 2: âphoto in âŠâ f1 s f2 s Feature 1 Feature 2 Feature 1 Feature 2 f1 s f2 s f: original features x: features after transformation Dimension of features x: Number of conditions * number of original features * number of buckets Learning the whole tree simultaneously == learning a linear function s =R(x)= <w, x> One condition Satisfied: Multiple condition Satisfied: ] 0 ,..., ' ,... 0 , ' ,... 0 [ : ij ij f f x
- 28. Outline Ranking model for graph search Learn the ranking model for graph search Training Data The Workflow to Learn Conditioned Bucket Ranker Learning To Rank Techniques Example Results on photo search
- 29. Learning to Rank Given lots of training data (feature_vector xi, score si), i=1,âŠn Learn a linear ranking function âą score = Ranking_model(feature_vector) = <w, x>
- 30. Learning to Rank -- History A good summary: http://en.wikipedia.org/wiki/Learning_to_rank Three main categories of methods âą Pointwise learning âą Pairwise learning âą Listwise learning
- 31. Learning to Rank -- History Pointwise Learning âą For one point (xi , si), the cost function is to make sure R(xi)ï si âą i.e., if one result gets clicked, its score should be close to 1; otherwise, its score should be close to 0; âą Every supervised regression or classification method are applicable. Examples: âą Linear, Logistic Regression, SVM, etc., âą FB Ads and Newsfeed ranking team are using methods in this category âą One possible problem: Label is not session specific X: feature_vector after transformation s: score R: ranking model
- 32. Learning to Rank -- History (Session Specific) Pairwise Learning âą For two points (xi, si), (xj, sj) from the same search session, the cost function is to make sure R(xi) > R(xj) , if si > sj âą In other words, if result i and j are results from the same search session, and i is clicked, but j is not, then score i should be higher than score j. âą Examples: RankNet, Ranking SVM , RankBoost, LambdaRank, CRR, etc. (Session Specific) Listwise Learning âą For all points from the same search session, make sure R(fi) will have the same order as si âą Structure SVM, LambdaMART, etc. X: feature_vector after transformation s: score R: ranking model
- 33. CRR: Combined Regression and Ranking pointwise term pairwise term regularization term E.g., f: linear, l:hinge loss, t: sign() or f: logistic function, l: a loga + (1-a) log(1-a), t: (1+y)/2
- 34. Learn (Conditioned) Bucket ranker Solve the problem by Stochastic Gradient Descent (SGD) methods âą Machine learning toolbox: sofia-ML Can train with 10M data within 1 hour in a single machine
- 35. Outline Ranking model for graph search Learn the ranking model for graph search Experiments and Discussions
- 36. Data Logging Logging of the results features at backend Logging of the click & conversion actions in frontend âą Tables from frontend and backend are joined to create the ultimate HIVE data table: search_learning_data âą Tables are populated once per day
- 37. Train Scoring Models with Machine Learning Pipelines Run two commands, e.g., âą search/ranking/util/collect_data_ta.sh '2013-08-21' '2013-08-27' -type users --output /home/jfh/data.txt âą search/ranking/util/train_model.sh /home/jfh/data.txt /home/jfh/bm_test Wait for 1-2 hours, obtain your model! Evaluation and A/B test âȘ More details: https://our.intern.facebook.com/intern/wiki/index.php/Trainin g_models_using_hive_data
- 38. Results ML Trained models for typeahead verticals (except group) are deployed in production âą Metrics are usually slightly better, compared to hand-tune model Two Trained Models for Browse deployed to production âą Ragtime: clickers by 2.47% and actioners by 3.77% âą Photos: more details in following slides
- 39. Basic metrics of the previous handtuned production model for photo search (# clicked sessions: 30.8% , # clicks per clicked session: 3.04) # clicks per session: 0.936, # likes per session: ~0.07 # comments: < 0.01
- 40. A/B test experiments compared to previous hand tuned production model Improvement compared to previous handtuned production model Target Actions #clicks Per session #likes per session #photo queries per day #photo search users per day click 5% -5% 3% ~1% like 4% >20% 0-1% ~0% click, like, comment 5% 7% 3% ~1% âȘ Run A/B test for handtuned model, and 3 machine trained models âȘ The three machine trained models have target actions of âclickâ, âlikeâ, âclick or like or commentâ respectively âȘ Run A/B tests for 3 weeks Deployed a our current production model.
- 41. Case Study â âphotos of mark zuckerbergâ Handtuned model Machine trained model One possible reason: improper feature weights In handtuned model, the weight of features FEATURE_CONSTRAINTS_RATIO (i.e., how many people tagged) is not as high as other features e.g., FEATURE_PHOTO_LIKES
- 42. Case Study ââphotos of Girish Kumarâs friendsâ Handtuned model Machine trained model One possible reason: âcounterâ-intuitive features In hand-tuned model, the older the photos, the lower the score In machine trained model, old photos gets low score, but very old photos (e.g.,>10 years) get highest score! Photos taken today Photos taken >10 years ago
- 43. More analysis on photo age photos of my friends, photos of a user photos of a product/company, movie, public figure, etc. photos liked a user photos in some place: almost 0 weights
- 44. More examples âPhotos liked by Lars Eilstrup Rasmussenâ Handtuned model Machine trained model âPhotos of Barack Obamaâ
- 45. More examples âPhotos by National Geographic â Handtuned model Machine trained model âPhotos in Beijing Chinaâ
- 46. Goal At the End of H2 Train models for all the use cases for all verticals Continuous training Cover corner cases well Offline feature extraction, evaluation Try different machine learning methods Easy to analyze and debug the machine learning and models
- 47. The Ultimate Goal Our life now âȘ Our life in the future