research-article

Job recommendation based on factorization machine and topic modelling

Authors:

Vasily Leksin,

Andrey OstapetsAuthors Info & Claims

RecSys Challenge '16: Proceedings of the Recommender Systems Challenge

Article No.: 6, Pages 1 - 4

https://doi.org/10.1145/2987538.2987542

Published: 15 September 2016 Publication History

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Abstract

This paper describes our solution for the RecSys Challenge 2016. In the challenge, several datasets were provided from a social network for business XING. The goal of the competition was to use these data to predict job postings that a user will interact positively with (click, bookmark or reply). Our solution to this problem includes three different types of models: Factorization Machine, item-based collaborative filtering, and content-based topic model on tags. Thus, we combined collaborative and content-based approaches in our solution. Our best submission, which was a blend of ten models, achieved 7th place in the challenge's final leader-board with a score of 1677 898.52. The approaches presented in this paper are general and scalable. Therefore they can be applied to another problem of this type.

References

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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
Upadhyay HSimsekler MMaalouf MAnwar SOmar M(2023)COVID-19, jobs and skills—Exploratory analysis of the job postings in the US and UK healthcare job marketPLOS ONE10.1371/journal.pone.027823718:1(e0278237)Online publication date: 20-Jan-2023
https://doi.org/10.1371/journal.pone.0278237
De DDwivedi RAllwani N(2023)Combined Application of Various Techniques for Personalized Job Recommendation2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)10.1109/ICECONF57129.2023.10083944(1-7)Online publication date: 5-Jan-2023
https://doi.org/10.1109/ICECONF57129.2023.10083944
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Index Terms

Job recommendation based on factorization machine and topic modelling
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Topic modeling
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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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 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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 September 2016

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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

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

Overall Acceptance Rate 11 of 15 submissions, 73%

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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
Upadhyay HSimsekler MMaalouf MAnwar SOmar M(2023)COVID-19, jobs and skills—Exploratory analysis of the job postings in the US and UK healthcare job marketPLOS ONE10.1371/journal.pone.027823718:1(e0278237)Online publication date: 20-Jan-2023
https://doi.org/10.1371/journal.pone.0278237
De DDwivedi RAllwani N(2023)Combined Application of Various Techniques for Personalized Job Recommendation2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)10.1109/ICECONF57129.2023.10083944(1-7)Online publication date: 5-Jan-2023
https://doi.org/10.1109/ICECONF57129.2023.10083944
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
He MWang TZhu YChen YYao FWang N(2021)FINN: Feature Interaction Neural Network for Person-Job Fit2021 7th International Conference on Big Data and Information Analytics (BigDIA)10.1109/BigDIA53151.2021.9619599(123-130)Online publication date: 29-Oct-2021
https://doi.org/10.1109/BigDIA53151.2021.9619599
Freire Mde Castro L(2020)e-Recruitment recommender systems: a systematic reviewKnowledge and Information Systems10.1007/s10115-020-01522-8Online publication date: 5-Nov-2020
https://doi.org/10.1007/s10115-020-01522-8
Rubtsov VKamenshchikov MValyaev ILeksin VIgnatov D(2018)A hybrid two-stage recommender system for automatic playlist continuationProceedings of the ACM Recommender Systems Challenge 201810.1145/3267471.3267488(1-4)Online publication date: 2-Oct-2018
https://dl.acm.org/doi/10.1145/3267471.3267488

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A preprocessing matrix factorization on collaborative filtering based library book recommendation system

A Collaborative Filtering Recommendation Algorithm Based on Item Classification

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