Talk at TD techtalk. 2016/04/26 http://eventdots.jp/event/584571

1. Recommendation 101
using Hivemall
Research Engineer
Makoto YUI @myui
<myui@treasure-data.com>
1
2016/04/25
Treasure Data Techtalk

2. Ø 2015.04 Joined Treasure Data, Inc.
1st Research Engineer in Treasure Data
Ø 2010.04-2015.03 Senior Researcher at National
Institute of Advanced Industrial Science and
Technology, Japan.
Ø 2009.03 Ph.D. in Computer Science from NAIST
Ø TD登山部部長
Ø 部員3名(うち幽霊部員1名）
Who am I ?
2

3. Ø 2015.04 Joined Treasure Data, Inc.
1st Research Engineer in Treasure Data
Ø 2010.04-2015.03 Senior Researcher at National
Institute of Advanced Industrial Science and
Technology, Japan.
Ø 2009.03 Ph.D. in Computer Science from NAIST
Ø TD登山部部長
Ø 部員3名(うち幽霊部員1名）
Who am I ?
3

4. Agenda
1. Short Introduction to Hivemall
2. Recommendation 101
3. Matrix Factorization
4. Factorization Machines
5. Bayesian Probabilistic Ranking
6. Conclusion
4

5. What is Hivemall
Scalable machine learning library built
as a collection of Hive UDFs, licensed
under the Apache License v2
5
https://github.com/myui/hivemall

6. 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
6

7. Ø CTR prediction of Ad click logs
•Freakout Inc., Fan communication, and more
•Replaced Spark MLlib w/ Hivemall at company X
Industry use cases of Hivemall
7
http://www.slideshare.net/masakazusano75/sano-hmm-20150512

8. 8
ØGender prediction of Ad click logs
•Scaleout Inc. and Fan commucations
http://eventdots.jp/eventreport/458208
Industry use cases of Hivemall

9. 9
Industry use cases of Hivemall
Ø Value prediction of Real estates
•Livesense
http://www.slideshare.net/y-ken/real-estate-tech-with-hivemall

10. 10Source: http://itnp.net/article/2016/02/18/2286.html
Industry use cases of Hivemall

11. 11
ØChurn Detection
•OISIX
Industry use cases of Hivemall
http://www.slideshare.net/TaisukeFukawa/hivemall-meetup-vol2-oisix

12. 12
会員サービスの解約予測
•10万人の会員による定期購
買が会社全体の売上、利益を
左右するが、解約リスクのあ
る会員を事前に把握、防止す
る策を欠いていた
•統計の専門知識無しで機械学習
•解約予測リストへのポイント付
与により解約率が半減
•解約リスクを伴う施策、イベン
トを炙り出すと同時に、非解約
者の特徴的な行動も把握可能に
•リスク度合いに応じて UI を変
更するなど間接的なサービス改
善も実現
•機械学習を行い、過去1ヶ月間
のデータをもとに未来1ヶ月間
に解約する可能性の高い顧客リ
ストを作成
•具体的には、学習用テーブル作
成 -> 正規化 -> 学習モデル作成
-> ロジスティック回帰の各ス
テップをTD + Hivemall を用い
てクエリで簡便に実現
Web
Mobile
属性情報
行動ログ
クレーム情報
流入元
利用サービス情報
直接施策
間接施策
ポイント付与 ケアコール
成功体験への誘導UI 変更
予測に使うデータ

13. 13
ØRecommendation
•Portal site
Industry use cases of Hivemall

14. List of Features in Hivemall v0.3.x
Classification(both
binary- and multi-class)
✓ Perceptron
✓ Passive Aggressive (PA)
✓ Confidence Weighted (CW)
✓ Adaptive Regularization of
Weight Vectors (AROW)
✓ Soft Confidence Weighted
(SCW)
✓AdaGrad+RDA
Regression
✓Logistic Regression (SGD)
✓PA Regression
✓AROW Regression
✓AdaGrad
✓AdaDELTA
kNN and Recommendation
✓ Minhash and b-Bit Minhash
(LSH variant)
✓ Similarity Search using K-NN
(Euclid/Cosine/Jaccard/Angular)
✓ Matrix Factorization
Feature engineering
✓ Feature Hashing
✓ Feature Scaling
(normalization, z-score)
✓TF-IDF vectorizer
✓Polynomial Expansion
Anomaly Detection
✓ Local Outlier Factor
Top-k query processing
14

15. Features supported in Hivemall v0.4.0
15
1.RandomForest
• classification, regression
2.Factorization Machine
• classification, regression (factorization)

16. Features supported in Hivemall v0.4.1-alpha
16
1. NLP Tokenizer (形態素解析)
• Kuromoji
2. Mini-batch Gradient Descent
3. RandomForest scalability Improvements
Treasure Data is operating
Hivemall v0.4.1-alpha.6
The above feature are already supported

17. Features supported in Hivemall v0.4.1
17
1. Matrix Factorization for Implicit Feedback
2. Field-aware Factorization Machines
V0.4.1-RC1 will be released soon!

18. Agenda
1. Short Introduction to Hivemall
2. Recommendation 101
3. Matrix Factorization
4. Factorization Machines
5. Bayesian Probabilistic Ranking
6. Conclusion
18

19. 19
Recommendation

20. 20
If I have 3 million customers on the Web,
I should have 3 million stores on the Web
Jeff Bezos, CEO of Amazon.com
Quote about Recommendation

21. •Explicit Feedback 明示的
• Item Rating
• Item Ranking
•Implicit Feedback 暗黙的
• Positive-only Implicit Feedback
• Bought (or not)
• Click (or not)
• Converged (or not)
21
Recommendation 101

22. •Explicit Feedback 明示的
• Item Rating
• Item Ranking
•Implicit Feedback 暗黙的
• Positive-only Implicit Feedback
• Bought (or not)
• Click (or not)
• Converged (or not)
22
Recommendation 101
評価用のPublicデータセットが存在し
サンプルやTutorial記事も多い

23. •Explicit Feedback 明示的
• Item Rating
• Item Ranking
•Implicit Feedback 暗黙的
• Positive-only Implicit Feedback
• Bought (or not)
• Click (or not)
• Converged (or not)
23
Recommendation 101
対応しているライブラリが少なく
サンプルが少ない

24. 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
24
Explicit Feedback

25. 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
25
Explicit Feedback

26. 26
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)

27. U/I Item 1 Item 2 Item 3 … Item I
User 1 ⭕ ⭕
User 2 ⭕ ⭕
… ⭕ ⭕
User U ⭕ ⭕ ⭕
27
Implicit Feedback

28. U/I Item 1 Item 2 Item 3 … Item I
User 1 ⭕ ⭕
User 2 ⭕ ⭕
… ⭕ ⭕
User U ⭕ ⭕ ⭕
28
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)

29. 29
Pros and Cons
Explicit
Feedback
Implicit
Feedback
Data size L J
User preference J L
Dislike/Unknown J L
Impact of Bias L J

30. Agenda
1. Short Introduction to Hivemall
2. Recommendation 101
3. Matrix Factorization
4. Factorization Machines
5. Bayesian Probabilistic Ranking
6. Conclusion
30

31. 31
Matrix Factorization/Completion
Factorize a matrix
into a product of matrices
having k-latent factor
K個の潜在変数

32. 32
Matrix Completion How-to
• Mean Rating μ
• Rating Bias for each Item Bi
• Rating Bias for each User Bu

33. 33
Mean Rating
Matrix Factorization
Regularization
Bias
for each user/item
Criteria of Biased MF
Factorization
Diff in prediction

34. 34
Training of Matrix Factorization
Support iterative training using local disk cache

35. 35
Prediction of Matrix Factorization

36. Agenda
1. Short Introduction to Hivemall
2. Recommendation 101
3. Matrix Factorization
4. Factorization Machines
5. Bayesian Probabilistic Ranking
6. Conclusion
36

37. 37
Factorization Machine
Matrix Factorization

38. 38
Factorization Machine
Context information (e.g., time)
can be considered
Source: http://www.ismll.uni-hildesheim.de/pub/pdfs/Rendle2010FM.pdf

39. 39
Factorization Machine
Factorization Model with degress=2 (2-way interaction)
Global Bias
Regression coefficience
of j-th variable
Pairwise Interaction
Factorization

40. 40
Training data for Factorization Machines
Each Feature takes LibSVM-like format <feature[:weight]>

41. 41
Training of Factorization Machines

42. 42
Prediction of Factorization Machines

43. Agenda
1. Short Introduction to Hivemall
2. Recommendation 101
3. Matrix Factorization
4. Factorization Machines
5. Bayesian Probabilistic Ranking
6. Conclusion
43

44. 44
Implicit Feedback
A naïve L approach by filling unknown cell
as negative

45. 45
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

46. •Rendle et al., “BPR: Bayesian Personalized Ranking
from Implicit Feedback”, Proc. UAI, 2009.
•A most proven(?) algorithm for recommendation for
implicit feedback
46
Bayesian Probabilistic Ranking
Key assumption: user u prefers item i over non-
observed item j

47. Bayesian Probabilistic Ranking
47
Image taken from
Rendle et al., “BPR: Bayesian Personalized Ranking from Implicit Feedback”, Proc. UAI, 2009.

48. 48
Bayesian Theorem
ベイズの定理: 事後確率は尤度と事前確率の積に比例
ここで、Θは求めたいパラメタ
>uはユーザuの好みの構造
ユーザuの好みの構造 >u が与えられたときの
モデルパラメタΘの事後分布P(Θ|>u)を得たい

49. 49
Bayesian Probabilistic Ranking
√
尤度
簡単のためにユーザの好み>uは互いに独立であるとして
p(i >u j|Θ)の直積で表現. 次のように尤度を定義
u,i,jを入力とした予測値にsigmoid関数を適用

50. 𝐵𝑃𝑅𝑂𝑃𝑇 = max
*
ln 𝑝 >/ |Θ 𝑝 Θ
50
Bayesian Probabilistic Ranking
= max
*
2 ln 𝜎（𝑥/567 (Θ)） − 𝜆 | 𝜎 |<
/,5,6 ∈?
√
√
√
ln sigmoid
for loss function
正則化

51. Train by BPR-Matrix Factoriaztion
51

52. 52
Predict by BPR-Matrix Factorization

53. 53
Predict by BPR-Matrix Factorization

54. 54
Predict by BPR-Matrix Factorization

55. 55
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

56. Agenda
1. Short Introduction to Hivemall
2. Recommendation 101
3. Matrix Factorization
4. Factorization Machines
5. Bayesian Probabilistic Ranking
6. Conclusion
56

57. Conclusion and Takeaway
57
Hivemall provides a collection of machine
learning algorithms as Hive UDFs/UDTFs
Treasure Data provides ML-as-a-Service
using latest version of Hivemall
• BPR-MF and BPR-FM (?) for Implicit Feedback
• Evaluation metric (NDCG, Prec/Recall@k)
• Field-aware Factorization Machine
• One-class Passive Aggressive
• (Kernelized Passive Aggressive)
Major features in the coming v0.4.1

58. 58
Blog article about Hivemall
http://blog-jp.treasuredata.com/
TD, Hivemall, Jupyter, Pandas-TDを使ってKaggleの
課題を解くシリーズ

59. 59
We support machine learning in Cloud
Any feature request? Or, questions?