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

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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)

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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

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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

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replies

ratings

1 2 3 4 5 6 7 8

12

34

5

Signal Comparison

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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

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Questions?

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Benjamin Kille (TU Berlin)

Competence Center Information Retrieval & Machine LearningInstitute of Commercial Information Technology and Quantitative Methods

benjamin.kille@tu-berlin.de@bennykille

http://crowdrec.eu

http://xing.com