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A bottom-up approach to job recommendation system

Published: 15 September 2016 Publication History

Abstract

Recommendation Systems are omnipresent on the web nowadays. Most websites today are striving to provide quality recommendations to their customers in order to increase and retain their customers. In this paper, we present our approaches to design a job recommendation system for a career based social networking website - XING. We take a bottom up approach: we start with deeply understanding and exploring the data and gradually build the smaller bits of the system. We also consider traditional approaches of recommendation systems like collaborative filtering and discuss its performance. The best model that we produced is based on Gradient Boosting algorithm. Our experiments show the efficacy of our approaches. This work is based on a challenge organized by ACM RecSys conference 2016. We achieved a final full score of 1,411,119.11 with rank 20 on the official leader board.

References

[1]
RecSys Challenge 2016 Official Website. Available at: http://2016.recsyschallenge.com/
[2]
RecSys Challenge 2016 GitHub. Available at: https://github.com/recsyschallenge/2016
[3]
J. B. Schafer, D. Frankowski, J. Herlocker and S. Sen. Collaborative filtering recommender systems. In The adaptive web 2007, pp. 291--324. Springer Berlin Heidelberg.
[4]
Caret: classification and regression training. Available at: https://cran.r-project.org/web/packages/caret/index.html
[5]
AppliedPredictiveModeling: Functions and Data Sets for `Applied Predictive Modeling'. Available at: https://cran.r-project.org/web/packages/AppliedPredictiveModeling/index.html

Cited By

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  • (2024)A Challenge-based Survey of E-recruitment Recommendation SystemsACM Computing Surveys10.1145/365994256:10(1-33)Online publication date: 22-Jun-2024
  • (2024)Resume Parser and Job Recommendation System using Machine Learning2024 International Conference on Emerging Systems and Intelligent Computing (ESIC)10.1109/ESIC60604.2024.10481635(157-162)Online publication date: 9-Feb-2024
  • (2024)Development of an expert system to overpass citizens technological barriers on smart home and livingProcedia Computer Science10.1016/j.procs.2023.10.048225:C(626-634)Online publication date: 4-Mar-2024
  • Show More Cited By

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Published In

cover image ACM Other conferences
RecSys Challenge '16: Proceedings of the Recommender Systems Challenge
September 2016
51 pages
ISBN:9781450348010
DOI:10.1145/2987538
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

  • Hungarian Academy of Sciences: The Hungarian Academy of Sciences
  • XING: XING AG
  • CrowdRec: CrowdRec

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 September 2016

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Author Tags

  1. collaborative filtering
  2. gradient boosting
  3. recommendation system
  4. regression

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  • Research-article

Conference

RecSys Challenge '16
Sponsor:
  • Hungarian Academy of Sciences
  • XING
  • CrowdRec

Acceptance Rates

RecSys Challenge '16 Paper Acceptance Rate 11 of 15 submissions, 73%;
Overall Acceptance Rate 11 of 15 submissions, 73%

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Cited By

View all
  • (2024)A Challenge-based Survey of E-recruitment Recommendation SystemsACM Computing Surveys10.1145/365994256:10(1-33)Online publication date: 22-Jun-2024
  • (2024)Resume Parser and Job Recommendation System using Machine Learning2024 International Conference on Emerging Systems and Intelligent Computing (ESIC)10.1109/ESIC60604.2024.10481635(157-162)Online publication date: 9-Feb-2024
  • (2024)Development of an expert system to overpass citizens technological barriers on smart home and livingProcedia Computer Science10.1016/j.procs.2023.10.048225:C(626-634)Online publication date: 4-Mar-2024
  • (2023)Job Recommendation Systems: A Literature ReviewInternational Journal of Innovative Science and Research Technology (IJISRT)10.38124/ijisrt/IJISRT23FEB061(2356-2359)Online publication date: 4-Apr-2023
  • (2020)Using autoencoders for session-based job recommendationsUser Modeling and User-Adapted Interaction10.1007/s11257-020-09269-1Online publication date: 1-Jul-2020
  • (2020)e-Recruitment recommender systems: a systematic reviewKnowledge and Information Systems10.1007/s10115-020-01522-8Online publication date: 5-Nov-2020
  • (2018)News Session-Based Recommendations using Deep Neural NetworksProceedings of the 3rd Workshop on Deep Learning for Recommender Systems10.1145/3270323.3270328(15-23)Online publication date: 6-Oct-2018
  • (2018)CHAMELEONProceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3240331(578-583)Online publication date: 27-Sep-2018
  • (2018)Neural Network Based Psychometric Analysis for Employability2018 International Conference on Research in Intelligent and Computing in Engineering (RICE)10.1109/RICE.2018.8509092(1-5)Online publication date: Aug-2018

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