Machine Learning for Recommender Systems in the Job Market
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The document discusses machine learning techniques for recommender systems in job markets. It describes the goals of recommending jobs that users are interested in and qualified for. It outlines challenges like understanding user profiles and job descriptions accurately. It then explains the recommender system architecture, which uses collaborative, content-based, and other filters to make recommendations by analyzing over 200 features of users and jobs. Evaluation results show that outlier filtering improves the quality of recommendations by reducing bad recommendations while keeping good ones.
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Machine Learning for Recommender Systems in the Job Market
- 1. Machine Learning for Recommender Systems in the Job Markethamburg.ai, May 2017 Fabian Abel
- 2. Challenge Given a user, the goal is to recommend job postings… 1. that the user may be interested in and 2. for which the user is an appropriate candidate. 2 Scala Dev (m/w) Scala Engineer Scala Dev, Hamburg user job postings Job recommende r companie s recruiter 19M 750k-1M
- 3. 3 Goals / Triangle of contradiction Scala Dev, Hamburg • Relevant recos • No spam • Relevant candidates • High reach • Happy customers • High revenue (e.g. many clicks on paid content) companie s user
- 8. 8
- 9. 9 Job Recommender REST Service GET /rest/recommendations/jobs/user/42 //response: { "total": 20, "collection":[ {"item_id": 7263, "score": 0.87, "reason": [..],..}, {"item_id": 6526, "score": 0.81, "reason": [..],..}, ... ] }
- 10. 10 Search indices XING Sources/XINGservices MySQL NoSQL live updates Batch processing batch updates Infrastructure for recommenders RecommenderRESTservice XING Products Deployment Infrastructure
- 11. 11 Search indices XING Sources/XINGservices MySQL NoSQL live updates Batch processing batch updates Infrastructure for recommenders RecommenderRESTservice XING Products Deployment Infrastructure
- 12. 12 Title Company Employment type and career level Full-text description Key properties of a job posting
- 13. 13 Key sources for understanding user demands 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, bookmarks, ratings, shown big data kununu Interactions of similar users similar usershadoop scala
- 14. 14 Relevance Estimation 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, bookmarks, ratings big data kununu Interactions of similar users similar usershadoop scala Content- based features Collaborative features Social features Usage behavior features Core RecSys engines (regression model) Logistic Regression P(relevant | x) = 1 1 + e-(b0 + bi xi)i n feature vector impact of feature xi
- 15. 15 Relevance Estimation + Additional Filters Content- based features Collaborative features Social features Usage behavior features Core RecSys engines (regression model) Location- based filtering Frequenty Shown Filtering Monetary- based diversification Career Level filtering Filtering & Diversification 0.92 0.8 0.76 … 4 core sub-recommender engines and 19 filters that together analyze and exploit around 200 features (relevance criteria) ...
- 16. 16 Collaborative filtering Theory: User-based and Item-based CF User-Item-Rating Matrix Anna 3 - 4 - 2 Julia 2 - 5 4 1 Tim 4 3 - 5 1 John - 4 5 4 - Java D. SAP Co Data En Data Sc BI Dev User-based CF: Compare users based on their ratings (e.g. cosine sim.) Use the n most similar users to predict a rating on an item Item-based CF: Compare items based on their ratings (e.g. cosine sim.) Use the n most similar items to predict a rating from a user (simple weight average)
- 17. 17 Collaborative filtering Reality: Ultra sparse User-Item Matrix and primarily implicit feedback Anna - - 1 - - Julia - - - - - Tim - - - - - John 1 - - - - Java D. SAP Co Data En Data Sc BI Dev High level of sparsity: classical collaborative fitering (or matrix factorization) does not work
- 18. 18 Collaborative filtering Reality: Ultra sparse User-Item Matrix and primarily implicit feedback Anna - - 1 - - Data Sci - - 32 18 - Tim 524 3 1 - - John - - 2 4 - Java D. SAP Co Data En Data Sc BI Dev Data Scientists Skilled in Java BI Dev Pseudo CF: Cluster users based on... jobrole skills field of study Recommend items that simillar users (= clusters) interacted with New item problem remains...
- 19. 19 Content-based filtering Example: semantic search Fabian Abel Data Mining Expert Haves: Interests: ML, j2ee Hadoop Raw profile Ontology-based Data Scientist Synonyms: Data Mining Expert, Data Mining Specialist, … 6940 263 JEE Synonyms: J2EE, Java Enterprise, … 370 Computer Science Synonyms: Informatik, Comp. Sci., CS, … 162 Hadoop Synonyms: Apache Hadoop, … 473 Machine Learning Synonyms: Maschinelles Lernen, … [jobrole] [skills] [field of studies] Education: Computer Sci. query TFxIDF
- 20. 20 Content-based filtering Example: more-like-this component Anna Bookmarked, rated and applied-to job postings 1 2 3 q = trans( 1 2 3 ) Recommending similar items q 7 8 9 R = 8 9 7 TFxIDF Re-rank by similarity of topic model vectors: R’ map { r => val x = B’ map { b => cosineSim(r, b) } r -> x.sum / x.size } sortBy(-_._2) 8 7 9 Re-ranking: - LSI - Word2Vec Topic model vector representations 1 2 7 8 3 9 1 2 8 3 97R’= B’= =B =R
- 21. 21 Content-based filtering Example: more-like-this component CTR TFxIDF LSI-based re-ranking +3.2% +3.1% Word2Vec-based Re-ranking
- 22. Challenges Issues that we have to fight with… 22
- 23. 23 Profiles vs. People’s wishes for their future past past Profile describes a user‘s past/current position(s), not future wishes
- 24. What John writes… 24 And what he means… Recruiter-John International Sales Manager Call Center Agent (10 EUR per hour) Sales Manager Sales Manager for B2B customers (80K EUR per year) Data Scientist skilled in Hadoop, Scala, Elasticsearch, … with PhD in … Data Analyst (skilled in SAS or Excel)
- 25. What Paul says he is… 25 And what he means… Paul, the Candidate CEO Network Engineer (currently unemployed) BI Engineer (skilled in old-school ETL) Shopman (in a kiosk) Data Scientist with 100+ skills Sales Manager
- 26. 26 Understanding the meaning of things that recruiters write in job postings and users write in their profiles is not trivial…
- 27. 27 People freak out if we recommend something wrong! Try to eliminate freakommendations (outliers)
- 28. Outlier Filtering Core RecSys engines Location Filter Outlier filterFiltering & Diversification 0.92 0.8 0.76 … Career level Filter ... … ... … 2. Filter: if (r > threshold) keep else drop 1. predictRating( , ) = predict(toFeatureVec( , ) = r //rating between 1 and 5 Estimate how a user would rate the item… (training: 750k explicit ratings)
- 29. good recos bad recos Percentageoffiltereduser-job postingpairsbyrating threshold 29 Example: with a threshold of 2.5 we kill 86% of the bad and 18% of the good recos Outlier Filtering The “filter onion”: trade-off between killing bad recos and keeping good ones
- 30. • xgboost-based model • Example features (137 features in total): • Matching & weighting: jobrole, skills, discipline, industry, ... • Distance: home location / job seeker location • Transitions: job role job role, field of study job role • ... 30 Outlier Filtering Example features (137 features in total)
- 31. Outlier Filtering Some A/B test results: user success 31 filtering userswithrecos no filtering -10.9% +7.4% userswhoclickedonrecos no filtering filtering Less people get recommendations, but more users click! Stricter filtering pays off!
- 32. ACM RecSys Challenge http::/recsyschallenge.com 32 Task: push recommendations (new items, paid vs. non- paid, premium vs. basic users) Started beginning of March (ca. 240 teams so far), ends in June Offline & online evaluation Still possible to sign-up for the offline evaluation…
Notas del editor
- Personalised recommendations based on the user‘s behaviour