We know where you should work next summer: job recommendations
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Talk given at RecSys 2015 about job recommendations and career path mining. Abstract: http://dl.acm.org/citation.cfm?id=2799496 Spin-off project for browsing career paths and statistics about professions: https://futureme.xing.com/
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We know where you should work next summer: job recommendations
- 1. We know where you should work next summer Job Recommendations September 2015 @RecSys Fabian Abel, http://xing.com
- 2. We know where you should work next summer Job Recommendations September 2015 @RecSys Fabian Abel, http://xing.comAt XING in Hamburg, Barcelona, Vienna or somewhere remote…
- 3. Challenge Identifying job postings that match the demands of the user and employer 3 Job Recommender 0.92 0.8 0.76 … User Job postingsEmployer
- 7. Job postings on XING 7 Title Company Employment type and career level Full-text description Key properties of a job posting
- 8. Key sources for understanding user demands Exploiting patterns that are found in the data(graph) 8 Social Network explicit and implicit connections Profile Fabian Abel Data Scientist Haves: Interests: web science big data, hadoop skills & co. Interactions data web social media clicks, shares, ratings big data kununu Interactions of similar users similar usershadoop scala
- 9. Social Network explicit and implicit connections Profile Fabian Abel Data Scientist Haves: Interests: web science big data, hadoop skills & co. Interactions data web social media clicks, shares, ratings big data kununu Interactions of similar users similar usershadoop scala Relevance Estimation Final relevance score of an item is obtained by combining the scores coming from the “sub-recommenders” (= features) 9 Content- based features Collaborative features Social features Usage behavior features Relevance Estimation (regression model) Logistic Regression P(relevant | x) = 1 1 + e-(b0 + bi xi)i n feature vector impact of feature xi
- 10. Relevance Estimation + Additional Filters Filtering (rules) may dampen the relevance scores or filter out items 10 Content- based features Collaborative features Social features Usage behavior features Relevance Estimation (regression model) Location- based filtering Content- based diversification Monetary- based diversification Career Level filtering Filtering & Diversification 0.92 0.8 0.76 … past past Profile describes a user‘s past/current position(s), not her future career step(s)
- 11. Career path patterns: locations Distance between user and location of bookmarked job postings on XING 11 0-50 km 35% 51-200 km 22% >200 km 43%
- 12. Career path patterns: career levels Climbing up the ladder (based on 15M XING CVs) 12 junior junior senior manger senior manger today Nextstep 53% senior 72% manger 54% senior manger 52%
- 13. Career path patterns: job roles Most users switch at least once from one job role to another 13 Postdoc Manager Lecturer Postdoc Professor 6% 5% 3% 2%
- 14. Career path transitions Understanding transitions in the career path graph 14 Web Developer J2EE Developer Data Scientist Machine Learning Expert MSc Computer Science CV: MSc Computer Science Web Developer J2EE Developer Data Scientist Machine Learning Expert Data Scientist Machine Learning Expert PhD Data Mining CV: J2EE Developer Data Scientist Machine Learning Expert MSc Computer Science CV: PhD Data Mining
- 15. Career path graph Weighted directed graph with different types of nodes (job roles, education) 15 Association rule mining for constructing the career path graph: • Association rules (= edges): Job role A Job role B Education X Job Role Y ... • Minimum support (e.g. at least k transitions with A and B have to occur in the data) • Minimum confidence (= probability(B | A) = weights of edges) MSc Computer Science Web Developer J2EE Developer Data Scientist Machine Learning Expert PhD Data Mining Similarly, graphs are constructed for: Jobrole X Industry Y Career Level X Career Level Y ... Thresholds for min-support and min- confidence need to be learned (per “discipline”)
- 16. Inferring Features from Career path graph(s) Probabilities that the job role is appropriate for the user 16 User Machine Learning Expert PhD Data Mining Job posting Data Scientist P( | , )Data Scientist Machine Learning Expert = 0.79F2: PhD Data Mining P( | )Data Scientist Machine Learning Expert = 0.52F1: Features: P( | , )Data Scientist Machine Learning Expert = 0.6F3: 5 years experience … Career path graph
- 17. Impact of Career Path feature AB test with 50:50 split, >10M impressions CTR Control group Group with Career path feature ? +8% 2% 1%
- 18. 18 futureme.xing.com futureme.xing.com spin-off project which allows for browsing the career-path graph
- 20. The professional network www.xing.com Thank you @fabianabel xing.com futureme.xing.com