research-article

A preliminary study on a recommender system for the job recommendation challenge

Authors:

Mirko Polato,

Fabio AiolliAuthors Info & Claims

RecSys Challenge '16: Proceedings of the Recommender Systems Challenge

Article No.: 1, Pages 1 - 4

https://doi.org/10.1145/2987538.2987549

Published: 15 September 2016 Publication History

Get Access

Abstract

In this paper we present our method used in the RecSys '16 Challenge.

In particular, we propose a general collaborative filtering framework where many predictors can be cast. The framework is able to incorporate information about the content but in a collaborative fashion. Using this framework we instantiate a set of different predictors that consider different aspects of the dataset provided for the challenge. In order to merge all these aspects together, we also provide a method able to linearly combine the predictors. This method learns the weights of the predictors by solving a quadratic optimization problem.

In the experimental section we show the performance using different predictors combinations. Results highlight the fact that the combination always outperforms the single predictor.

References

[1]

F. Aiolli. Efficient top-N recommendation for very large scale binary rated datasets. In ACM Recommender Systems Conference, pages 273--280, Hong Kong, China, 2013.

Digital Library

Google Scholar

[2]

F. Aiolli. Convex AUC optimization for top-N recommendation with implicit feedback. In ACM Recommender Systems Conference, pages 293--296, New York, USA, 2014.

Digital Library

Google Scholar

[3]

R. P. F. Abel, D. Kohlsdorf. Acm recsys challenge 2016: Training data. https://recsys.xing.com/data, 2016.

Google Scholar

[4]

Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In ICDM, pages 263--272, 2008.

Digital Library

Google Scholar

[5]

R. Pan, Y. Zhou, B. Cao, N. N. Liu, R. Lukose, M. Scholz, and Q. Yang. One-class collaborative filtering. In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, ICDM '08, pages 502--511, Washington, DC, USA, 2008. IEEE Computer Society.

Digital Library

Google Scholar

[6]

S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI '09, pages 452--461, Arlington, Virginia, United States, 2009. AUAI Press.

Digital Library

Google Scholar

[7]

S. Sedhain, A. Menon, S. Sanner, and D. Braziunas. On the effectiveness of linear models for one-class collaborative filtering, 2016.

Google Scholar

Cited By

View all

Mao YLin SCheng Y(2024)A Job Recommendation Model Based on a Two-Layer Attention MechanismElectronics10.3390/electronics1303048513:3(485)Online publication date: 24-Jan-2024
https://doi.org/10.3390/electronics13030485
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
He MShen DWang TZhao HZhang ZHe R(2023)Self-Attentional Multi-Field Features Representation and Interaction Learning for Person–Job FitIEEE Transactions on Computational Social Systems10.1109/TCSS.2021.313445810:1(255-268)Online publication date: Feb-2023
https://doi.org/10.1109/TCSS.2021.3134458
Show More Cited By

Index Terms

A preliminary study on a recommender system for the job recommendation challenge
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

Recommendations

Local Item-Item Models For Top-N Recommendation
RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems

Item-based approaches based on SLIM (Sparse LInear Methods) have demonstrated very good performance for top-N recommendation; however they only estimate a single model for all the users. This work is based on the intuition that not all users behave in ...
A Scalable, Accurate Hybrid Recommender System
WKDD '10: Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data Mining

Recommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given resource. There are three main types of recommender systems: collaborative filtering, content-based filtering, and ...
Improving Accuracy of Recommender System by Item Clustering

Recommender System (RS) predicts user's ratings towards items, and then recommends highly-predicted items to user. In recent years, RS has been playing more and more important role in the agent research field. There have been a great deal of researches ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

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 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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 September 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

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%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
325
Total Downloads

Downloads (Last 12 months)10
Downloads (Last 6 weeks)1

Reflects downloads up to 03 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Mao YLin SCheng Y(2024)A Job Recommendation Model Based on a Two-Layer Attention MechanismElectronics10.3390/electronics1303048513:3(485)Online publication date: 24-Jan-2024
https://doi.org/10.3390/electronics13030485
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
He MShen DWang TZhao HZhang ZHe R(2023)Self-Attentional Multi-Field Features Representation and Interaction Learning for Person–Job FitIEEE Transactions on Computational Social Systems10.1109/TCSS.2021.313445810:1(255-268)Online publication date: Feb-2023
https://doi.org/10.1109/TCSS.2021.3134458
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 MZhu YLv NHe R(2022)A Feature Fusion-based Representation Learning Model for Job Recommendation2022 2nd International Conference on Consumer Electronics and Computer Engineering (ICCECE)10.1109/ICCECE54139.2022.9712756(791-794)Online publication date: 14-Jan-2022
https://doi.org/10.1109/ICCECE54139.2022.9712756
Pérez Maurera FFerrari Dacrema MSaule LScriminaci MCremonesi Pd'Aquin MDietze SHauff CCurry ECudre Mauroux P(2020)ContentWise ImpressionsProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3412774(3093-3100)Online publication date: 19-Oct-2020
https://dl.acm.org/doi/10.1145/3340531.3412774
Polato MAiolli F(2018)Boolean kernels for collaborative filtering in top-N item recommendationNeurocomputing10.1016/j.neucom.2018.01.057286:C(214-225)Online publication date: 19-Apr-2018
https://dl.acm.org/doi/10.1016/j.neucom.2018.01.057

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Local Item-Item Models For Top-N Recommendation

A Scalable, Accurate Hybrid Recommender System

Improving Accuracy of Recommender System by Item Clustering