2016/12/07 IEEE BigData 2016での、風間の講演資料になります

1. Company Recommendation for New
Graduates via Implicit Feedback
Multiple Matrix Factorization with
Bayesian Optimization
IEEE BIG DATA
2016 Washington D.C.
Masahiro Kazama1, Issei Sato2, Haruaki Yatabe3,
Tairiku Ogihara3, Tetsuro Onishi3, Hiroshi Nakagawa2
1.Recruit Technologies Co., Ltd. 2. University of Tokyo
3.Recruit Career Co., Ltd

2. Outline
• Problem Settings
• Data Description
• Proposed Method
• Experiments
• Results
• Conclusion

3. Problem Setting
• Unique job hunting activities of Japanese students
• The starting time for job hunting is fixed
• All students apply at the same time
Example. job hunting schedule of students who graduate in 2015
Start job hunting activities Start Interview Graduate/Join
Dec 1, 2013 April 1, 2014 April 1, 2015

4. Problem Setting
• Students have to send application sheet for many
companies to get a job offer
• Many students spend much time on job hunting
activities. This is a big social problem in Japan
• Many students send application sheet to the
popular companies at the beginning. But they have
a high competition rate, therefore they can not get
a job offer.

5. Popularity bias
• Browsing concentrates on some companies
5Company(ordered by popularity)
Low-browsed companies (Bottom 80%)
High-browsed companies（Top 20%)
Number of Students

6. Problem Setting
• It is important to find a company suitable for
students at an early stage of job hunting activities
• It is important to consider not only High-browsed
companies but also Low-browsed companies

7. Solutions
• We recommend suitable companies to students at
an early stage
• We focus on low-browsed companies

8. Data
• Our company (Recruit.Co.Ltd) provides a job
recruiting service
• Almost all students use our service
• We have three types of data
1. Browsing data
2. Entry data
3. Student/Company information

9. Browsing data
• Browsing data of students on our recruiting service
• Used for training our model
• period： 2013/12/1〜2014/3/31
9

10. Entry data
• Entry data of students on our recruiting service
• Used for evaluating our model
• period： 2013/12/1〜2014/3/31
10

11. Browsing (click) data
11
click i1 i2 i3 i4
j1 0 4 0 21
j2 71 31 0 18
j3 3 1 2 0
Students
Company

12. Entry data
12
entry i1 i2 i3 i4
j1 0 1 0 0
j2 0 1 0 1
j3 1 0 1 0
Student
Company

13. Student/Company info
13
Student
Faculty
Department
etc..
Company
Industry type
Location
Number of employees

14. Overview
14
Purpose
Solution
・Using browsing data and student/company information,
we recommend suitable companies to students
・We focus on low-browsed companies
• Using browsing data -> Implicit feedback recommendation
• Low-browsed item recommendation -> Popularity bias
• Hyper parameter search → Bayesian optimization

15. Explicit VS Implicit
15
Explicit feedback Implicit feedback
The data user
explicitly give.
The user action data for
guessing user
preference
e.g. Amazon 5 star rating Click log
Pros Good quality Easy to get
Much data
Con Difficult to get Noise
Popularity bias

16. Popularity bias
• Browsing concentrates on some companies
→High-browsed companies are more likely to be
recommended
16Company(ordered by popularity)
Low-browsed company (Bottom 80%)
We want to recommend these
High-browsed company（Top 20%)
Number of students

17. Implicit feedback matrix factorization
17
Number of clicks
Collaborative Filtering for Implicit Feedback Datasets(2008)
Yifan Hu, Yehuda Koren, Chris Volinsky
rui =
1
0
rui > 0
rui = 0
!
"
#
$#
confidence
preference
i1 i2 i3
j1 41
j2 2
j3 24 3 51
Browsing data

18. Problem
• High-browsed companies are more likely to be
recommended
18
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10 i11 i12
Company
Number
of
clicks
Low-browsed companies
We want to recommend these
Likely to be recommended

19. Proposed method
19
= Number of users who browsed the company i
（Company’s popularity)
c is bigger when the company has fewer clicks
→ Low-browsed companies are likely to be
recommended

20. Proposed method with side
information
20
Student
information
Company
information

21. Hyper parameter search
• Weight of Browsing α、β、Regularization λ1, λ2, λ3
• When the number of hyper parameter is large, grid
search doesn’t work well
• Use Bayesian optimization for hyper parameter search
21

22. Bayesian optimization
22
x y=f(x) y
Optimization for Black-box
→Gaussian process is assumed for distribution of function f(x)
→It suggests the next hyper parameter to evaluate
x : Hyper parameter α、β、λ1, λ2, λ3
f(x) : Recall
We want to find hyper parameter that maximize Recall
Mockus,1978

23. Data and Evaluation
Recall@100(low browsed)
23
c01 c02 c03 c04 c05 c06 c07 c08 c09 c10
Brow
sing
10 20 1 8 5 10 3 7 23 13
Entry ◯ ◯ ◯ ◯
60% 20% 20%
Training Set for matrix factorization
Validation Set for Bayesian Optimization(BO)
Evaluation Set

24. Results
0 0.1 0.2 0.3 0.4 0.5
BO+Hu et al.
BO+Fang et al.
Proposed
Proposed with side
Proposed models get better recall

25. Trials of Bayesian Optimization
Increasing the trials, we get better recall.
-> we can find better hyper parameters

26. Conclusions
• We built a recommendation system that relaxes
popularity bias
• By using the side information, the recommendation
performance of the low-browsed companies
improved
• Hyper parameter optimization was performed
using Bayesian optimization