Employer Name Normalization in the Online Recruitment Industry

on YouTube ↗01h 33m

" summary

CompanyDepot links employer strings from jobs and resumes to a CareerBuilder KB using Lucene indexing, query expansion from external mappings (Wikipedia redirects and stock tickers), top 1,000 retrieval followed by heuristic filtering and a learning‑to‑rank model (coordinate ascent via RankLib optimizing P@1) and a binary validator. It adds graph-based clustering across five mapping sources with transitive closure and low‑quality edge pruning, selects cluster representatives by LTR, and reports 2.5%-21.4% higher coverage at equal precision plus better success rate and reduced diversity in candidate‑search facets.

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" chapters (48)

1. 0:00Intro
2. 4:30Precision Agriculture
3. 9:10Farms
4. 9:42Drone Animation
5. 10:19Sensor Placement
6. 11:23Sensors
7. 12:18Gaussian Processes
8. 14:38Drones
9. 16:21Power Consumption
10. 16:56Battery Usage
11. 17:56Background Drones
12. 18:33UAV Control Algorithm
13. 19:19Networking Challenge
14. 19:29Wireless Technologies
15. 20:06Television White Spaces
16. 21:32Work in Kenya
17. 23:10Drone hack
18. 26:51Datadriven farming
19. 28:50Resources
20. 29:55Research
21. 30:16About you
22. 30:36Apache Spark
23. 32:07ML Lib
24. 33:06MLM Deep Learning
25. 34:11Sparx Pipeline
26. 37:32Hierarchy of Abstractions
27. 38:36Deconvolution
28. 39:52Compositional Hierarchy
29. 42:40Applications
30. 44:40Weight sharing
31. 45:05combinatorial flexibility
32. 45:36automated feature engineering
33. 46:48compositional structure
34. 47:06representation
35. 47:19ML API
36. 48:18Modularity
37. 48:49Momentum
38. 49:18RMS Problem
39. 49:50SparkMe
40. 50:02SparkDL Library
41. 53:02Challenges
42. 54:24Cluster level normalization
43. 54:41Overall architecture
44. 55:05Entitylevel normalization
45. 56:05Retrieval
46. 56:49Ranking
47. 58:10Validation
48. 58:54Normalization

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