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Explaining and Exploring Job Recommendations:
a User-driven Approach for Interacting with
Knowledge-based Job Recommender ...
22
• Abundant overload of job vacancies
• Dynamic Labor Market: need to support job mobility
• Providing effective recomme...
Explaining job recommendations to show competence match
Support exploration and user control over broad and diverse recomm...
Research Questions
4
[RQ1] Does enabling job seekers to interact with
visualization techniques empower them to explore,
un...
5
Interactive & Job Recommender Systems
SetFusion (Parra et al., 2014)
LinkedVis (Bostandjiev, 2013)
JobStreet (Bakri et a...
Labor Market Explorer
Interactive dashboard to support job seekers
Explore and explain recommendations - actionable insigh...
User-Centered Design Process
7
8
Ranking of parameters as voted by participants
Co-design sessions
9
Labor Market Explorer design goals
10
[DG1] Exploration/Control
Job seekers should be able to control
recommendations and ...
[DG1] Exploration/Control
11
[DG2] Explanations
12
[DG3] Actionable insights
13
Final evaluation
66 job seekers (age 33.9 ± 9.5, 18F)
8 training programs, 4 groups, 1 hour.
1
2
3
4
5
6
7
8
ResQue questi...
User feedback
15
User feedback
16
User feedback
17
Interaction patterns
18
Interaction patterns
19
Interaction patterns
20
Discussion
[RQ1] User Empowerment
• The approach is perceived as effective to explore job recommendations.
• Most particip...
[RQ2] Personal Characteristics
• The explorer was slightly better perceived by older participants (45+).
• Participants in...
User-centered design process involving both job seekers
and job mediators
Key features:
Design implications
23
• Overview ...
• Future work will focus on a “simulation mode”.
• Further investigate job mobility scenarios.
• Explore “What-If” scenari...
Explaining and Exploring Job Recommendations:
a User-driven Approach for Interacting with
Knowledge-based Job Recommender ...
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Explaining and Exploring Job Recommendations: a User-driven Approach for Interacting with Knowledge-based Job Recommender Systems

Presented at RecSys 2019

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Explaining and Exploring Job Recommendations: a User-driven Approach for Interacting with Knowledge-based Job Recommender Systems

  1. 1. Explaining and Exploring Job Recommendations: a User-driven Approach for Interacting with Knowledge-based Job Recommender Systems Francisco Gutiérrez, Sven Charleer, Robin De Croon, Nyi Nyi Htun, Gerd Goetschalckx, Katrien Verbert Computer Science Department, KU Leuven http://augment.cs.kuleuven.be francisco.gutierrez@cs.kuleuven.be @FranciscoGhz 1 katrien.verbert@cs.kuleuven.be @katrien_v AUGMENT
  2. 2. 22 • Abundant overload of job vacancies • Dynamic Labor Market: need to support job mobility • Providing effective recommendations particularly challenging. • Need for: increased diversity explanations user control exploration Problem: interaction with job RecSys needed
  3. 3. Explaining job recommendations to show competence match Support exploration and user control over broad and diverse recommendations Approach
  4. 4. Research Questions 4 [RQ1] Does enabling job seekers to interact with visualization techniques empower them to explore, understand, and find job recommendations? [RQ2] Do personal characteristics, such as age and background, impact the user perception and user interaction with such an interface?
  5. 5. 5 Interactive & Job Recommender Systems SetFusion (Parra et al., 2014) LinkedVis (Bostandjiev, 2013) JobStreet (Bakri et al., 2017)
  6. 6. Labor Market Explorer Interactive dashboard to support job seekers Explore and explain recommendations - actionable insights
  7. 7. User-Centered Design Process 7
  8. 8. 8 Ranking of parameters as voted by participants
  9. 9. Co-design sessions 9
  10. 10. Labor Market Explorer design goals 10 [DG1] Exploration/Control Job seekers should be able to control recommendations and filter out the information flow coming from the recommender engine by prioritizing specific items of interest. [DG2] Explanations Recommendations and matching scores should be explained, and details should be provided on- demand. [DG3] Actionable Insights The interface should provide actionable insights to help job-seekers find new or more job recommendations from different perspectives.
  11. 11. [DG1] Exploration/Control 11
  12. 12. [DG2] Explanations 12
  13. 13. [DG3] Actionable insights 13
  14. 14. Final evaluation 66 job seekers (age 33.9 ± 9.5, 18F) 8 training programs, 4 groups, 1 hour. 1 2 3 4 5 6 7 8 ResQue questionnaire + two open questions. Users explored the tool freely. All interactions were logged. 14
  15. 15. User feedback 15
  16. 16. User feedback 16
  17. 17. User feedback 17
  18. 18. Interaction patterns 18
  19. 19. Interaction patterns 19
  20. 20. Interaction patterns 20
  21. 21. Discussion [RQ1] User Empowerment • The approach is perceived as effective to explore job recommendations. • Most participants felt confident and will use the explorer again. • Explanations contribute to support user empowerment. • A diverse set of actionable insights were also mentioned by participants. 21
  22. 22. [RQ2] Personal Characteristics • The explorer was slightly better perceived by older participants (45+). • Participants in the technical group engaged more with all the different features of the dashboard. • Non-native speakers, sales and construction groups engaged more with the map. • The table overview was perceived as very useful by all user groups, but the interaction may need further simplification for some users. Discussion 22
  23. 23. User-centered design process involving both job seekers and job mediators Key features: Design implications 23 • Overview first, favorite competences of interest. • Competence-based explanations. • Actionable, job market related insights. • Diverse set of filters.
  24. 24. • Future work will focus on a “simulation mode”. • Further investigate job mobility scenarios. • Explore “What-If” scenarios. • Autonomous exploration of the job market. Future work 24
  25. 25. Explaining and Exploring Job Recommendations: a User-driven Approach for Interacting with Knowledge-based Job Recommender Systems Francisco Gutiérrez, Sven Charleer, Robin De Croon, Nyi Nyi Htun, Gerd Goetschalckx, Katrien Verbert francisco.gutierrez@cs.kuleuven.be @FranciscoGhz 25 katrien.verbert@cs.kuleuven.be @katrien_v AUGMENT

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