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Content-Based approaches for Cold-Start Job Recommendations

Published: 27 August 2017 Publication History

Abstract

This paper provides an overview of the approach we adopted as team Lunatic Goats for the ACM RecSys Challenge 2017 [7]. The competition, organized by XING.com, focuses on a cold start job recommendation scenario. The goal was to design and tune a recommendation system able to predict past users' interactions, for the offline stage, and to provide recommendations pushed every day to real users through the XING portal, for the online stage. Our strategy, which saw models coming from different techniques combined in a multi-layer ensemble, granted us the first place in the offline part and the qualification as second best team in the final leaderboard. All our algorithms mainly resort to content-based approaches, that, thanks to its ability to provide good recommendations even for cold-start items allowed us, quite unexpectedly, to achieve good results in terms of prediction quality and computational time.

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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)Towards Flexible and Adaptive Neural Process for Cold-Start RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.330483936:4(1815-1828)Online publication date: Apr-2024
  • (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
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    cover image ACM Other conferences
    RecSys Challenge '17: Proceedings of the Recommender Systems Challenge 2017
    August 2017
    44 pages
    ISBN:9781450353915
    DOI:10.1145/3124791
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    • XING: XING AG

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    New York, NY, United States

    Publication History

    Published: 27 August 2017

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    Author Tags

    1. ACM RecSys Challenge 2017
    2. Cold-Start recommendations
    3. Content-Based Filtering
    4. Job recommendations
    5. Recommendation Systems

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    RecSys Challenge '17
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    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)Towards Flexible and Adaptive Neural Process for Cold-Start RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.330483936:4(1815-1828)Online publication date: Apr-2024
    • (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
    • (2023)Personalized ASD Interventions Recommendation for Sri Lankan Patients2023 5th International Conference on Advancements in Computing (ICAC)10.1109/ICAC60630.2023.10417605(774-779)Online publication date: 7-Dec-2023
    • (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
    • (2021)Systematic Review of Nutritional Recommendation SystemsApplied Sciences10.3390/app11241206911:24(12069)Online publication date: 17-Dec-2021
    • (2020)Why Are Deep Learning Models Not Consistently Winning Recommender Systems Competitions Yet?Proceedings of the Recommender Systems Challenge 202010.1145/3415959.3416001(44-49)Online publication date: 26-Sep-2020
    • (2020)Using autoencoders for session-based job recommendationsUser Modeling and User-Adapted Interaction10.1007/s11257-020-09269-130:4(617-658)Online publication date: 1-Jul-2020
    • (2018)Efficient Context-Aware Sequential Recommender SystemCompanion Proceedings of the The Web Conference 201810.1145/3184558.3191581(1391-1394)Online publication date: 23-Apr-2018

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