using interaction signals for job recommendation
TRANSCRIPT
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 2015slide 2/26
Problem Description
Using Interaction Signals for Job Recommendation | Benjamin Kille | SIREMTI 2015slide 3/26
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Traditional Method to Look for a Job
Using Interaction Signals for Job Recommendation | Benjamin Kille | SIREMTI 2015slide 4/26
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Tends in Job Seeking
Using Interaction Signals for Job Recommendation | Benjamin Kille | SIREMTI 2015slide 5/26
Use of print media decreasesProfessionals 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
Weitzel et al. (2015). Bewerbungspraxis 2015 – Eine empirische Studie mit 7000 Stellensuchenden und Karriereinteressierten im Internet.
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Reciprocal Selection
Using Interaction Signals for Job Recommendation | Benjamin Kille | SIREMTI 2015slide 6/26
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 2015slide 7/26
Types of FeedbackWe track users ...
... clicking on
... bookmarking
... replying to
suggested job offers
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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 2015slide 13/26
A User Inquiry
– How satisfied are users with their job recommendations?
– Collect ratings for job recommendations
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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 2015slide 19/26
Relation: ratings ~ clicks
Using Interaction Signals for Job Recommendation | Benjamin Kille | SIREMTI 2015slide 20/26
clicks
ratings
1 2 3 4 5 6 7 8 9 10
12
34
5
Relation: ratings ~ bookmarks
Using Interaction Signals for Job Recommendation | Benjamin Kille | SIREMTI 2015slide 21/26
Ratings for Bookmarked Jobs (μ = 3.6)
rating
Den
sity
0 1 2 3 4 5
0.0
0.1
0.2
0.3
0.4
0.5
Relation: ratings ~ replies
Using Interaction Signals for Job Recommendation | Benjamin Kille | SIREMTI 2015slide 22/26
replies
ratings
1 2 3 4 5 6 7 8
12
34
5
Signal Comparison
Using Interaction Signals for Job Recommendation | Benjamin Kille | SIREMTI 2015slide 23/26
clicks
ratings
1 2 3 4 5 6 7 8 9 10
12
34
5
Ratings for Bookmarked Jobs (μ = 3.6)
rating
Den
sity
0 1 2 3 4 5
0.0
0.1
0.2
0.3
0.4
0.5
replies
ratings
1 2 3 4 5 6 7 8
12
34
5
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 2015slide 24/26
Questions?
Using Interaction Signals for Job Recommendation | Benjamin Kille | SIREMTI 2015slide 25/26
Using Interaction Signals for Job Recommendation | Benjamin Kille | SIREMTI 2015slide 26/26
Benjamin Kille (TU Berlin)
Competence Center Information Retrieval & Machine LearningInstitute of Commercial Information Technology and Quantitative Methods
[email protected]@bennykille
http://crowdrec.eu
http://xing.com