Using Interaction Signals for Job Recommendation
Benjamin Kille, Fabian Abel, Balázs Hidasi, Sahin Albayrak| SIREMTI | 13 November 2015

Agenda
– Looking for a Job: now and then
– Data Description
– User Inquiry
– Findings
– Conclusion and Outlook
Using Interaction Signals for Job Recommendation | Benjamin Kille | SIREMTI 2015
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Problem Description
Using Interaction Signals for Job Recommendation | Benjamin Kille | SIREMTI 2015
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http://peacechild.org/wp-content/uploads/2015/09/Youth-unemployment.jpg

Traditional Method to Look for a Job
Using Interaction Signals for Job Recommendation | Benjamin Kille | SIREMTI 2015
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http://www.businessreviewaustralia.com/public/uploads/large/large_article_im640_newspaper_ad_2.jpg

Tends in Job Seeking
Using Interaction Signals for Job Recommendation | Benjamin Kille | SIREMTI 2015
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Use of print media decreases
Professionals predominantly use
– Online job offer collectors
– Online business networks
Trend leads to
– Higher volume of job offers to
process for professionals
– Higher volume of candidates to
deal with for employers
– Reciprocal selection problem
Weitzelet al. (2015). Bewerbungspraxis 2015 – Eine empirische Studie mit 7000 Stellensuchenden und Karriereinteressierten im Internet.
http://3.bp.blogspot.com/-Tm8S-4mJcdQ/Tx2Y-ND1flI/AAAAAAAACcM/l6fPkkR9RMI/s1600/information_overload_hydrant.jpg

Reciprocal Selection
Using Interaction Signals for Job Recommendation | Benjamin Kille | SIREMTI 2015
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Professional:
– reduce job offers to manageable size
– remove irrelevant job offers
– keep relevant job offers
Recruiter:
– reduce list of candidates to
manageable pool
– keep candidates with required skills
– keep candidates likely to respond
– remove candidates lacking necessary
skills
Ideally: match needs of both parties

How do we select job offers/candidates?
1. learn a modell representing professionals’ requirements
– curriculum vitae/skills
– location
– preferences
2. apply modell to available job offers
3. present suggestions to professionals
4. observe how professionals react
5. adjust modell to improve suggestions (repeat)
Using Interaction Signals for Job Recommendation | Benjamin Kille | SIREMTI 2015
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Types of Feedback
We track users ...
... clicking on
... bookmarking
... replying to
suggested job offers
Using Interaction Signals for Job Recommendation | Benjamin Kille | SIREMTI 2015
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Which Type of Feedback should we use?
What can a click tell us?
What can a bookmark tell us?
What can replies tell us?
Using Interaction Signals for Job Recommendation | Benjamin Kille | SIREMTI 2015
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A User Inquiry
– How satisfied are users with their job recommendations?
– Collect ratings for job recommendations
Using Interaction Signals for Job Recommendation | Benjamin Kille | SIREMTI 2015
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What Type of Feedback Tells us Most?
Idea: Check which kind of Feedback correlates best with ratings:
– ratings ~ clicks
– ratings ~ bookmarks
– ratings ~ replies
Using Interaction Signals for Job Recommendation | Benjamin Kille | SIREMTI 2015
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Relation: ratings ~ clicks
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clicks
ratings
1 2 3 4 5 6 7 8 9 10
12345

Relation: ratings ~ bookmarks
Using Interaction Signals for Job Recommendation | Benjamin Kille | SIREMTI 2015
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Ratings for Bookmarked Jobs (μ = 3.6)
rating
Density
0 1 2 3 4 5
0.00.10.20.30.40.5

Relation: ratings ~ replies
Using Interaction Signals for Job Recommendation | Benjamin Kille | SIREMTI 2015
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replies
ratings
1 2 3 4 5 6 7 8
12345

Signal Comparison
Using Interaction Signals for Job Recommendation | Benjamin Kille | SIREMTI 2015
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clicks
ratings
1 2 3 4 5 6 7 8 9 10
12345
Ratings for Bookmarked Jobs (μ = 3.6)
rating
Density
0 1 2 3 4 5
0.00.10.20.30.40.5
replies
ratings
1 2 3 4 5 6
12345

Conclusion and Outlook
feedback is necessary to improve recommendations
analysis of three signals:
– clicks à few clicks might be misleading
– bookmarks à filter bad suggestions; concentrate on medium preferences
– replies à most accurately reflect preferences
next steps
– implement a recommendation strategy that learns with replies
– A/B testing to verify suitability
Using Interaction Signals for Job Recommendation | Benjamin Kille | SIREMTI 2015
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Questions?
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Benjamin Kille (TU Berlin)
Competence Center Information Retrieval & Machine Learning
Institute of Commercial Information Technology and Quantitative Methods
benjamin.kille@tu-berlin.de
@bennykille
http://crowdrec.eu
http://xing.com