Using solr to find the right person for the right job - By Kang Laura
1. Using Solr to find the right person for the right job MaY 2011 Laura Kang Theladders.com
2. Agenda Search at the Ladders Current Projects Standardization of search products Recommendation service Suggested Candidates Testing Sessions Challenges and Future Work
3. TheLadders.com $100K+ job search and career management Right person for the right job, right job for the right person >4 million members Recruit Ladder Community of recruitersand job seekers Job search advisors and talent specialists
4. Search at the Ladders January 2010: search team formed. First member: computational linguist, Dr. Leslie Barrett February 2011: platform team formed. 9 members Search, CMS, shared services Led by Ed Cudahy
5. Solr/Luceneat the Ladders Search Subscriber search Candidate search Recruiter search Job search Hiring alerts search Editorial content search Suggestions Job to Candidate Candidate to Job Recruiter to Candidate Recruiters like this Candidates like this Jobs like this
6. Standardization of Search Products Before Legacy implementations Local Luceneindex Different versions of Solr Hard to make improvements Move to Solr 3.1.0
8. Challenges Power users Large Boolean queries Target result set of 50 Synonyms with different lengths HR manager vs. Human Resource manager Phrase slop Company variants Communicating to users
9. Typical Query Title: "sales consultant" "sales engineer" "solutions architect" "solution architect" engineer architect "pre-sales consultant" "presales consultant" "solutions engineer" -president -vp -cmo -ceo -cfo -chief -director -software -"s/w" –database Location: Redwood City, CA, 100 miles Company: apple "ingram micro" "tech data" intermec "insight enterprises" "super micro computer" "digi international inc" radisys "silicon graphics international corp" crayangilysysvoltaire "concurrent computer" gtsi "socket mobile" hphewletthitachiibm "i. b. m." "i.b.m." "international business machines" "business machines" intevacxyratex quantum "western digital" wdw.d. ramtron "micron technology" e.m.c. "e. m. c." sandisk brocade seagate 3par stec "dot hill" oczo.c.z. "o. c. z." "hutchinson technology" "hutchinson tech" lasercardedcidataram "overland storage" emcnetapp Keyword: (presales "pre sales" pre-sales sales) AND (engineer engineering) AND (storage hardware "sun microsystems" "sun servers")
10. Recommendation Service RESTful web service Solr backend Tracking/feedback A/B framework Client Application Recommendation Service Job to Candidate Candidate to Job Recruiter to Candidate Recruiters like this Candidates like this Jobs like this
11. Other Projects Resume parsing Parse flagging Company list Company variants Similar companies Synonym list generation Search help for users
12. Suggested Candidates Coming soon on Recruit Ladder http://recruit.theladders.com Suggest candidates that might be a good match when a recruiter posts a job Goal: Help recruiters quickly identify candidates that are right for the job Increase interaction between recruiters and job seekers
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17. Suggested Candidates Hybrid Categories Position level Job specialties/disciplines Industry Job title => position level (VP, director) + job function (marketing) Relevance matrices for partial matches Text Job function keywords Candidate’s job experience vs. job description using MoreLikeThis Boosting Filters: salary range, years of experience, location
18. Challenges Certifications and skills Taxonomy “Good” resume Resume scoring Job description Short job descriptions “Series 7 & 63 and 10+ years of experience in related field required” Company description/Equal Employment Opportunity clauses Different priorities for each job type
19. Evaluation Other products: Mean-average precision: search A/B testing: suggested jobs Testing sessions with talent specialists and external recruiters Randomly selected set of jobs Given 5 candidates and their resumes 0 = not a good match 1 = keep in pipeline 2 = contact
20. Future Work Job description parser Skills and certifications UI improvements Personalized weights and filters Company description Geospatial features Recommendation Service: user ratings Search/Recommendation quality testing framework Customer satisfaction metrics Pre-release metrics