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Multi-stack ensemble for job recommendation

Authors:

Massimo QuadranaAuthors Info & Claims

RecSys Challenge '16: Proceedings of the Recommender Systems Challenge

Article No.: 8, Pages 1 - 4

https://doi.org/10.1145/2987538.2987541

Published: 15 September 2016 Publication History

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Abstract

This paper describes the approach that team PumpkinPie adopted in the 2016 Recsys Challenge. The task of the competition organized by XING is to predict which job postings the user has interacted with. The team's approach mainly consists in generating a set of models using different techniques, and then combining them in a multi-stack ensemble. This strategy granted the fourth position in the final leader-board to the team, with an overall score of 1.86M.

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Cited By

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Mashayekhi YLi NKang BLijffijt JDe Bie T(2024)A Challenge-based Survey of E-recruitment Recommendation SystemsACM Computing Surveys10.1145/365994256:10(1-33)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3659942
Perez Maurera FFerrari Dacrema MCremonesi P(2022)Towards the Evaluation of Recommender Systems with ImpressionsProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3551483(610-615)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1145/3523227.3551483
Li SChuancheng YYinggang LHongguo WYanhui D(2018)information to Intelligence(itoI): A Prototype for Employment Prediction of Graduates Based on Multidimensional Data2018 9th International Conference on Information Technology in Medicine and Education (ITME)10.1109/ITME.2018.00187(834-836)Online publication date: Oct-2018
https://doi.org/10.1109/ITME.2018.00187
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Information & Contributors

Information

Published In

RecSys Challenge '16: Proceedings of the Recommender Systems Challenge

September 2016

51 pages

ISBN:9781450348010

DOI:10.1145/2987538

Conference Chairs:
Fabian Abel
XING AG, Germany
,
András Benczúr
Hungarian Academy of Sciences, Hungary
,
Daniel Kohlsdorf
XING AG, Germany
,
Martha Larson
TU Delft and Radboud University Nijmegen, Netherlands
,
Róbert Pálovics
Hungarian Academy of Sciences, Hungary

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 the author(s) 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].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 September 2016

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Research-article

Conference

RecSys Challenge '16

Sponsor:

Hungarian Academy of Sciences
XING
CrowdRec

RecSys Challenge '16: ACM RecSys 2016 Recommender Systems Challenge

September 15, 2016

Massachusetts, Boston, USA

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

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Mashayekhi YLi NKang BLijffijt JDe Bie T(2024)A Challenge-based Survey of E-recruitment Recommendation SystemsACM Computing Surveys10.1145/365994256:10(1-33)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3659942
Perez Maurera FFerrari Dacrema MCremonesi P(2022)Towards the Evaluation of Recommender Systems with ImpressionsProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3551483(610-615)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1145/3523227.3551483
Li SChuancheng YYinggang LHongguo WYanhui D(2018)information to Intelligence(itoI): A Prototype for Employment Prediction of Graduates Based on Multidimensional Data2018 9th International Conference on Information Technology in Medicine and Education (ITME)10.1109/ITME.2018.00187(834-836)Online publication date: Oct-2018
https://doi.org/10.1109/ITME.2018.00187
Schmitt TGonard FCaillou PSebag M(2017)Language Modelling for Collaborative Filtering: Application to Job Applicant Matching2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI.2017.00186(1226-1233)Online publication date: Nov-2017
https://doi.org/10.1109/ICTAI.2017.00186

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Multi-Behavior Job Recommendation with Dynamic Availability

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