PROSPECT: a system for screening candidates for recruitment

Companies often receive thousands of resumes for each job posting and employ dedicated screeners to short list qualified applicants. In this paper, we present PROSPECT, a decision support tool to help these screeners shortlist resumes efficiently. Prospect mines resumes to extract salient aspects of candidate profiles like skills, experience in each skill, education details and past experience. Extracted information is presented in the form of facets to aid recruiters in the task of screening. We also employ Information Retrieval techniques to rank all applicants for a given job opening. In our experiments we show that extracted information improves our ranking by 30% there by making screening task simpler and more efficient.

[1]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[2]  Jorge Nocedal,et al.  Representations of quasi-Newton matrices and their use in limited memory methods , 1994, Math. Program..

[3]  Jaana Kekäläinen,et al.  IR evaluation methods for retrieving highly relevant documents , 2000, SIGIR '00.

[4]  Andrei Z. Broder,et al.  Identifying and Filtering Near-Duplicate Documents , 2000, CPM.

[5]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[6]  Maria T. Pazienza,et al.  Information Extraction , 2002, Lecture Notes in Computer Science.

[7]  H. Cunningham,et al.  A framework and graphical development environment for robust NLP tools and applications. , 2002, ACL 2002.

[8]  Wo-Shun Luk,et al.  A framework for web table mining , 2002, WIDM '02.

[9]  Yalin Wang,et al.  A machine learning based approach for table detection on the web , 2002, WWW '02.

[10]  Tim Weitzel,et al.  An Automated Recommendation Approach to Selection in Personnel Recruitment , 2003, AMCIS.

[11]  W. Bruce Croft,et al.  Table extraction using conditional random fields , 2003, DG.O.

[12]  Fernando Pereira,et al.  Shallow Parsing with Conditional Random Fields , 2003, NAACL.

[13]  Pradeep Ravikumar,et al.  Adaptive Name Matching in Information Integration , 2003, IEEE Intell. Syst..

[14]  William W. Cohen,et al.  Semi-Markov Conditional Random Fields for Information Extraction , 2004, NIPS.

[15]  Chaomei Chen,et al.  Mining the Web: Discovering knowledge from hypertext data , 2004, J. Assoc. Inf. Sci. Technol..

[16]  Claudio Giuliano,et al.  A Critical Survey of the Methodology for IE Evaluation , 2004, LREC.

[17]  Kun Yu,et al.  Resume Information Extraction with Cascaded Hybrid Model , 2005, ACL.

[18]  James Allan,et al.  Matching resumes and jobs based on relevance models , 2007, SIGIR.

[19]  Tobias Keim,et al.  Extending the Applicability of Recommender Systems: A Multilayer Framework for Matching Human Resources , 2007, 2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07).

[20]  In Lee An architecture for a next-generation holistic e-recruiting system , 2007, CACM.

[21]  Tim Weitzel,et al.  Decision support for team staffing: An automated relational recommendation approach , 2008, Decis. Support Syst..

[22]  W. Bruce Croft,et al.  Discovering key concepts in verbose queries , 2008, SIGIR '08.

[23]  Sven Laumer,et al.  Help to find the needle in a haystack: integrating recommender systems in an IT supported staff recruitment system , 2009, SIGMIS CPR '09.

[24]  Jianying Hu,et al.  Leveraging social networks for corporate staffing and expert recommendation , 2009, IBM J. Res. Dev..

[25]  Evgueni A. Haroutunian,et al.  Information Theory and Statistics , 2011, International Encyclopedia of Statistical Science.