research-article

Job recommendation with Hawkes process: an effective solution for RecSys Challenge 2016

Authors:

Jun WangAuthors Info & Claims

RecSys Challenge '16: Proceedings of the Recommender Systems Challenge

Article No.: 11, Pages 1 - 4

https://doi.org/10.1145/2987538.2987543

Published: 15 September 2016 Publication History

Abstract

The RecSys Challenge 2016 focuses on the prediction of users' interest in clicking a job posting in the career-oriented social networking site Xing. Given users' profile, the content of the job posting, as well as the historical activities of users, we aim in recommending top job postings to users for the coming week. This paper introduces the winning strategy for such a recommendation task. We summarize several key components that result in our leading position in this contest. First, we build a hierarchical pairwise model with ensemble learning as the overall prediction framework. Second, we integrate both content and behavior information in our feature engineering process. In particular, we model the temporal activity pattern using a self-exciting point process, namely Hawkes Process, to generate the most relevant recommendation at the right moment. Finally, we also tackle the challenging cold start issue using a semantic based strategy that is built on the topic modeling with the users profiling information. Our approach achieved the highest leader-board and full scores among all the submissions.

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Cited By

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
Bied GNathan SPerennes EHoffmann MCaillou PCrépon BGaillac CSebag MElkind E(2023)Toward job recommendation for allProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/655(5906-5914)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/655
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
Show More Cited By

Index Terms

Job recommendation with Hawkes process: an effective solution for RecSys Challenge 2016
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Learning to rank
    2. Retrieval tasks and goals
      1. Recommender systems
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Boosting

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Information & Contributors

Information

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

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

Copyright © 2016 ACM.

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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Research-article

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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

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

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
Bied GNathan SPerennes EHoffmann MCaillou PCrépon BGaillac CSebag MElkind E(2023)Toward job recommendation for allProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/655(5906-5914)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/655
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
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
Lacic EReiter-Haas MKowald DReddy Dareddy MCho JLex E(2020)Using autoencoders for session-based job recommendationsUser Modeling and User-Adapted Interaction10.1007/s11257-020-09269-1Online publication date: 1-Jul-2020
https://doi.org/10.1007/s11257-020-09269-1
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
Shang JSun M(2019)Local low-rank Hawkes processes for modeling temporal user–item interactionsKnowledge and Information Systems10.1007/s10115-019-01379-6Online publication date: 9-Jul-2019
https://doi.org/10.1007/s10115-019-01379-6
Shang JSun M(2018)Local Low-Rank Hawkes Processes for Temporal User-Item Interactions2018 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM.2018.00058(427-436)Online publication date: Nov-2018
https://doi.org/10.1109/ICDM.2018.00058
Chen HChung CHuang HTsui W(2017)Common Pitfalls in Training and Evaluating Recommender SystemsACM SIGKDD Explorations Newsletter10.1145/3137597.313760119:1(37-45)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1145/3137597.3137601
Sato MNagatani KTahara T(2017)Exploring an Optimal Online Model for New Job RecommendationProceedings of the Recommender Systems Challenge 201710.1145/3124791.3124797(1-5)Online publication date: 27-Aug-2017
https://dl.acm.org/doi/10.1145/3124791.3124797
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