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This slide shows how to use Hivemall for Recommendation problems.

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- 1. Recommendation 101 using Hivemall Research Engineer Makoto YUI @myui <myui@treasure-data.com> 1
- 2. Agenda 1. Introduction to Hivemall 2. Recommendation 101 3. Matrix Factorization 4. Bayesian Probabilistic Ranking 2
- 3. What is Hivemall Scalable machine learning library built as a collection of Hive UDFs, licensed under the Apache License v2 3 https://github.com/myui/hivemall
- 4. Hivemall’s Vision: ML on SQL Classification with Mahout CREATE TABLE lr_model AS SELECT feature, -- reducers perform model averaging in parallel avg(weight) as weight FROM ( SELECT logress(features,label,..) as (feature,weight) FROM train ) t -- map-only task GROUP BY feature; -- shuffled to reducers ✓Machine Learning made easy for SQL developers (ML for the rest of us) ✓Interactive and Stable APIs w/ SQL abstraction This SQL query automatically runs in parallel on Hadoop 4
- 5. How to use Hivemall Machine Learning Training Prediction Prediction Model Label Feature Vector Feature Vector Label Data preparation 5
- 6. CREATE EXTERNAL TABLE e2006tfidf_train ( rowid int, label float, features ARRAY<STRING> ) ROW FORMAT DELIMITED FIELDS TERMINATED BY '¥t' COLLECTION ITEMS TERMINATED BY ",“ STORED AS TEXTFILE LOCATION '/dataset/E2006-tfidf/train'; How to use Hivemall - Data preparation Define a Hive table for training/testing data 6
- 7. How to use Hivemall Machine Learning Training Prediction Prediction Model Label Feature Vector Feature Vector Label Feature Engineering 7
- 8. create view e2006tfidf_train_scaled as select rowid, rescale(target,${min_label},${max_label}) as label, features from e2006tfidf_train; Applying a Min-Max Feature Normalization How to use Hivemall - Feature Engineering Transforming a label value to a value between 0.0 and 1.0 8
- 9. How to use Hivemall Machine Learning Training Prediction Prediction Model Label Feature Vector Feature Vector Label Training 9
- 10. How to use Hivemall - Training CREATE TABLE lr_model AS SELECT feature, avg(weight) as weight FROM ( SELECT logress(features,label,..) as (feature,weight) FROM train ) t GROUP BY feature Training by logistic regression map-only task to learn a prediction model Shuffle map-outputs to reduces by feature Reducers perform model averaging in parallel 10
- 11. How to use Hivemall - Training CREATE TABLE news20b_cw_model1 AS SELECT feature, voted_avg(weight) as weight FROM (SELECT train_cw(features,label) as (feature,weight) FROM news20b_train ) t GROUP BY feature Training of Confidence Weighted Classifier Vote to use negative or positive weights for avg +0.7, +0.3, +0.2, -0.1, +0.7 Training for the CW classifier 11
- 12. How to use Hivemall Machine Learning Training Prediction Prediction Model Label Feature Vector Feature Vector Label Prediction 12
- 13. How to use Hivemall - Prediction CREATE TABLE lr_predict as SELECT t.rowid, sigmoid(sum(m.weight)) as prob FROM testing_exploded t LEFT OUTER JOIN lr_model m ON (t.feature = m.feature) GROUP BY t.rowid Prediction is done by LEFT OUTER JOIN between test data and prediction model No need to load the entire model into memory 13
- 14. 14 Classification ✓ Perceptron ✓ Passive Aggressive (PA, PA1, PA2) ✓ Confidence Weighted (CW) ✓ Adaptive Regularization of Weight Vectors (AROW) ✓ Soft Confidence Weighted (SCW) ✓ AdaGrad+RDA ✓ Factorization Machines ✓ RandomForest Classification Regression ✓Logistic Regression (SGD) ✓PA Regression ✓AROW Regression ✓AdaGrad (logistic loss) ✓AdaDELTA (logistic loss) ✓Factorization Machines ✓RandomForest Regression List of supported Algorithms
- 15. List of supported Algorithms 15 Classification ✓ Perceptron ✓ Passive Aggressive (PA, PA1, PA2) ✓ Confidence Weighted (CW) ✓ Adaptive Regularization of Weight Vectors (AROW) ✓ Soft Confidence Weighted (SCW) ✓ AdaGrad+RDA ✓ Factorization Machines ✓ RandomForest Classification Regression ✓Logistic Regression (SGD) ✓AdaGrad (logistic loss) ✓AdaDELTA (logistic loss) ✓PA Regression ✓AROW Regression ✓Factorization Machines ✓RandomForest Regression SCW is a good first choice Try RandomForest if SCW does not work Logistic regression is good for getting a probability of a positive class Factorization Machines is good where features are sparse and categorical ones
- 16. List of Algorithms for Recommendation 16 K-Nearest Neighbor ✓ Minhash and b-Bit Minhash (LSH variant) ✓ Similarity Search on Vector Space (Euclid/Cosine/Jaccard/Angular) Matrix Completion ✓ Matrix Factorization ✓ Factorization Machines (regression) each_top_k function of Hivemall is useful for recommending top-k items
- 17. Other Supported Algorithms 17 Anomaly Detection ✓ Local Outlier Factor (LoF) Feature Engineering ✓Feature Hashing ✓Feature Scaling (normalization, z-score) ✓ TF-IDF vectorizer ✓ Polynomial Expansion (Feature Pairing) ✓ Amplifier NLP ✓Basic Englist text Tokenizer ✓Japanese Tokenizer (Kuromoji)
- 18. Agenda 1. Introduction to Hivemall 2. Recommendation 101 3. Matrix Factorization 4. Bayesian Probabilistic Ranking 18
- 19. •Explicit Feedback • Item Rating • Item Ranking •Implicit Feedback • Positive-only Implicit Feedback • Bought (or not) • Click (or not) • Converged (or not) 19 Recommendation 101
- 20. •Explicit Feedback • Item Rating • Item Ranking •Implicit Feedback • Positive-only Implicit Feedback • Bought (or not) • Click (or not) • Converged (or not) 20 Recommendation 101 Case for Coursehero?
- 21. U/I Item 1 Item 2 Item 3 … Item I User 1 5 3 User 2 2 1 … 3 4 User U 1 4 5 21 Explicit Feedback
- 22. U/I Item 1 Item 2 Item 3 … Item I User 1 ? 5 ? ? 3 User 2 2 ? 1 ? ? … ? 3 ? 4 ? User U 1 ? 4 ? 5 22 Explicit Feedback
- 23. 23 Explicit Feedback U/I Item 1 Item 2 Item 3 … Item I User 1 ? 5 ? ? 3 User 2 2 ? 1 ? ? … ? 3 ? 4 ? User U 1 ? 4 ? 5 • Very Sparse Dataset • # of feedback is small • Unknown data >> Training data • User preference to rated items is clear • Has negative feedbacks • Evaluation is easy (MAE/RMSE)
- 24. U/I Item 1 Item 2 Item 3 … Item I User 1 ⭕ ⭕ User 2 ⭕ ⭕ … ⭕ ⭕ User U ⭕ ⭕ ⭕ 24 Implicit Feedback
- 25. U/I Item 1 Item 2 Item 3 … Item I User 1 ⭕ ⭕ User 2 ⭕ ⭕ … ⭕ ⭕ User U ⭕ ⭕ ⭕ 25 Implicit Feedback • Sparse Dataset • Number of Feedbacks are large • User preference is unclear • No negative feedback • Known feedback maybe negative • Unknown feedback maybe positive • Evaluation is not so easy (NDCG, Prec@K, Recall@K)
- 26. 26 Pros and Cons Explicit Feedback Implicit Feedback Data size L J User preference J L Dislike/Unknown J L Impact of Bias L J
- 27. Agenda 1. Introduction to Hivemall 2. Recommendation 101 3. Matrix Factorization 4. Bayesian Probabilistic Ranking 27
- 28. 28 Matrix Factorization/Completion Factorize a matrix into a product of matrices having k-latent factor
- 29. 29 Matrix Completion How-to • Mean Rating μ • Rating Bias for each Item Bi • Rating Bias for each User Bu
- 30. 30 Mean Rating Matrix Factorization Regularization Bias for each user/item Criteria of Biased MF Factorization Diff in prediction
- 31. 31 Training of Matrix Factorization Support iterative training using local disk cache
- 32. 32 Prediction of Matrix Factorization
- 33. Agenda 1. Introduction to Hivemall 2. Recommendation 101 3. Matrix Factorization 4. Bayesian Probabilistic Ranking 33 Still in Beta but will officially be supported soon
- 34. 34 Implicit Feedback A naïve L approach by filling unknown cell as negative
- 35. 35 Sampling scheme for Implicit Feedback Sample pairs <u, i, j> of Positive Item i and Negative Item j for each User u • Uniform user sampling Ø Sample a user. Then, sample a pair. • Uniform pair sampling Ø Sample pairs directory (dist. along w/ original dataset) • With-replacement or without-replacement sampling U/I Item 1 Item 2 Item 3 … Item I User 1 ⭕ ⭕ User 2 ⭕ ⭕ … ⭕ ⭕ User U ⭕ ⭕ ⭕ Default Hivemall sampling scheme: - Uniform user sampling - With replacement
- 36. •Rendle et al., “BPR: Bayesian Personalized Ranking from Implicit Feedback”, Proc. UAI, 2009. •A most proven(?) algorithm for recommendation for implicit feedback 36 Bayesian Probabilistic Ranking Key assumption: user u prefers item i over non- observed item j
- 37. Bayesian Probabilistic Ranking 37 Image taken from Rendle et al., “BPR: Bayesian Personalized Ranking from Implicit Feedback”, Proc. UAI, 2009. http://www.algo.uni-konstanz.de/members/rendle/pdf/Rendle_et_al2009-Bayesian_Personalized_Ranking.pdf BPRMF’s task can be considered filling 0/1 the item-item matrix and getting probability of I >u J
- 38. Train by BPR-Matrix Factoriaztion 38
- 39. 39 Predict by BPR-Matrix Factorization
- 40. 40 Predict by BPR-Matrix Factorization
- 41. 41 Predict by BPR-Matrix Factorization
- 42. 42 Recommendation for Implicit Feedback Dataset 1. Efficient Top-k computation is important for prediction O(U * I) 2. Memory consumption is heavy for where item size |i| is large • MyMediaLite requires lots of memory • Maximum data size of Movielens: 33,000 movies by 240,000 users, 20 million ratings 3. Better to avoid computing predictions for each time
- 43. 43 We support machine learning in Cloud Any feature request? Or, questions?

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