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Online Event Recommendation for Event-based Social Networks

Authors:

Li GuoAuthors Info & Claims

WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web

Pages 45 - 46

https://doi.org/10.1145/2740908.2742742

Published: 18 May 2015 Publication History

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Abstract

With the rapid growth of event-based social networks, the demand of event recommendation becomes increasingly important. While, the existing event recommendation approaches are batch learning fashion. Such approaches are impractical for real-world recommender systems where training data often arrive sequentially. Hence, we present an online event recommendation method. Experimental results on several real-world datasets demonstrate the utility of our method.

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Cited By

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Wu DXiao EZhu YJensen CLu K(2023) Efficient Retrieval of the Top- k Most Relevant Event-Partner Pairs IEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.311855235:3(2529-2543)Online publication date: 1-Mar-2023
https://doi.org/10.1109/TKDE.2021.3118552
Liang YHuang CBao XXu K(2021)Sequential dynamic event recommendation in event-based social networks: An upper confidence bound approachInformation Sciences10.1016/j.ins.2020.06.047542(1-23)Online publication date: Jan-2021
https://doi.org/10.1016/j.ins.2020.06.047
Bok KLee SChoi DLee DYoo J(2019)Recommending personalized events based on user preference analysis in event based social networksElectronic Commerce Research10.1007/s10660-019-09335-wOnline publication date: 6-Feb-2019
https://doi.org/10.1007/s10660-019-09335-w
Show More Cited By

Index Terms

Online Event Recommendation for Event-based Social Networks
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web

May 2015

1602 pages

ISBN:9781450334730

DOI:10.1145/2740908

General Chairs:
Aldo Gangemi
National Research Council, Italy & Paris 13 University-CNRS, France
,
Stefano Leonardi
Sapienza University of Rome, Italy
,
Alessandro Panconesi
Sapienza University of Rome, Italy

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 May 2015

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Funding Sources

the Strategic Leading Science and Technology Projects of CAS
973 project
NSFC
Australia ARC Discovery Project

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WWW '15

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IW3C2

WWW '15: 24th International World Wide Web Conference

May 18 - 22, 2015

Florence, Italy

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Wu DXiao EZhu YJensen CLu K(2023) Efficient Retrieval of the Top- k Most Relevant Event-Partner Pairs IEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.311855235:3(2529-2543)Online publication date: 1-Mar-2023
https://doi.org/10.1109/TKDE.2021.3118552
Liang YHuang CBao XXu K(2021)Sequential dynamic event recommendation in event-based social networks: An upper confidence bound approachInformation Sciences10.1016/j.ins.2020.06.047542(1-23)Online publication date: Jan-2021
https://doi.org/10.1016/j.ins.2020.06.047
Bok KLee SChoi DLee DYoo J(2019)Recommending personalized events based on user preference analysis in event based social networksElectronic Commerce Research10.1007/s10660-019-09335-wOnline publication date: 6-Feb-2019
https://doi.org/10.1007/s10660-019-09335-w
Wu DZhu YJensen C(2019)In Good Company: Efficient Retrieval of the Top-k Most Relevant Event-Partner PairsDatabase Systems for Advanced Applications10.1007/978-3-030-18579-4_31(519-535)Online publication date: 24-Apr-2019
https://doi.org/10.1007/978-3-030-18579-4_31
梁媛(2018)A Survey on Data Management of Event-Based Social NetworksComputer Science and Application10.12677/CSA.2018.8916008:09(1459-1481)Online publication date: 2018
https://doi.org/10.12677/CSA.2018.89160
Shang YXu KHan YZhang C(2016)FTM: Recommending the Right Items for User Temporal Interests with Matrix Factorization through Topic Model2016 IEEE First International Conference on Data Science in Cyberspace (DSC)10.1109/DSC.2016.20(189-198)Online publication date: Jun-2016
https://doi.org/10.1109/DSC.2016.20

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Context-Aware Event Recommendation in Event-based Social Networks

Collaborative Social Group Influence for Event Recommendation

Event Recommendation Based on Heterogeneous Social Network Information and Time Information

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