6th Data Science Leuven Meetup

1. How searching for a job is
like being on Tinder
Recommender systems
Leuven Data Science meetup - 27/03/2018
Michael Reusens
michaelreusens@hotmail.com

2. Overview
• Who am I?
• What do I do?
o Recommender systems
• Why would you care?
o Lessons learned
2

3. About me
3
• Computer scientist
• PhD Student Applied Economics
o Leuven Institute for Research on information Systems,
Faculty of Business and Economics, KU Leuven
• Study recommender systems
o Collaboration with public employment services (VDAB)

4. Recommender system
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“Recommender systems are the set of software tools that, given a user and a set of
items, suggest those items that are probably relevant to the user” (Resnick et al., 1997)

5. Recommender system
5
“Recommender systems are the set of software tools that, given a user and a set of
items, suggest those items that are probably relevant to the user” (Resnick et al., 1997)

6. Recommender system
6
“Recommender systems are the set of software tools that, given a user and a set of
items, suggest those items that are probably relevant to the user” (Resnick et al., 1997)

7. Recommender system
7
“Recommender systems are the set of software tools that, given a user and a set of
items, suggest those items that are probably relevant to the user” (Resnick et al., 1997)

8. Research context
8

9. Research context
9
+
Recommender systems?

10. Research context
• 1,000,000 vacancies between November 2014 -October
2015 (Arvastat, 2015)
 Information overflow problem
https://arvastat.vdab.be/10

11. Research context
• 1,000,000 vacancies between November 2014 -October
2015 (Arvastat, 2015)
 Information overflow problem
 Solution: Recommender systems (RSs)
11 https://arvastat.vdab.be/

12. Reciprocity in job recommendation and
search
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• Getting a job (or job seeker) is based on reciprocated
interest
Job providersJob seekers
interest
interest

13. Study
13
VDAB is interested in adding a job RS

14. Study
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VDAB is interested in adding a job RS
1. Properties of a good job RS
o Predicts user interest better
o Recommends more items that reciprocate interest
o …

15. Study
15
VDAB is interested in adding a job RS
1. Properties of a good job RS
o Predicts user interest better
o Recommends more items that reciprocate interest
o …
2. How does recommended content differ from searched
content?

16. 16

17. Project 2: motivation
17

18. Experimental set-up
18
• Looked at most popular RS algorithms
o Collaborative filtering, SVD-based, … (usual suspects)
o Interest Prediction, Reciprocity

19. Experimental set-up
19
• Looked at most popular RS-algorithms
o Collaborative filtering, SVD-based, … (usual suspects)
o Interest Prediction, Reciprocity
• Compared this to reciprocity of search

20. What we found
20
• Looked at most popular RS-algorithms
o They predict the next job-click quite well
o < 1% of recommended jobs reciprocate interest
Job providersJob seekers < 1%
Pretty good

21. What we found
21
• Looked at most popular RS-algorithms
o They predict the next job-click quite well
o < 1% of recommended jobs reciprocate interest
• Compared this to reciprocity of search
o +- 15% of searched jobs reciprocate interest

22. What we found
22
• Looked at most popular RS-algorithms
o They predict the next job-click quite well
o < 1% of recommended jobs reciprocate interest
• Compared this to reciprocity of search
o +- 15% of searched jobs reciprocate interest
• Using these standard RSs could hurt the job seeker!
< 1% <<< +-15%

23. Solution
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• Finding a job (or job seeker) is kind of like finding a partner
interest

24. Solution
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• Finding a job (or job seeker) is kind of like finding a partner
• Looked at RSs developed for date recommendation
o Used in various online dating apps
o Tailored for reciprocity
interest

25. What we found
25
• Looked at RS developed for date recommendation
o Slightly worse interest prediction
o +- 37% Reciprocity!! (= much better)
Job providersJob seekers 37%
Slightly worse

26. What we learned
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1. You might be working on a new, unique, problem
NOBODY
UNDERSTANDS
MY PROBLEMS


27. What we learned
27
1. You might be working on a new, unique, problem
o Lesson learned: Try to find similar problems that are
solved

28. What we learned
28
1. You might be working on a new, unique, problem
o Lesson learned: Try to find similar problems that are
solved
2. Data science allows us to create new services

29. What we learned
29
1. You might be working on a new, unique, problem
o Lesson learned: Try to find similar problems that are
solved
2. Data science allows us to create new services
o Lesson learned: Be wary for non-obvious
consequences of new services

30. Thank you for your attention!
• My recommendation
30
interest

31. Thank you for your attention!
• My recommendation
31
interest
• Questions
• Suggestions
• Thoughts
• ...
Contact: michaelreusens@hotmail.com
About our research: www.datamingapps.com

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