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Deep Modeling of the Evolution of User Preferences and Item Attributes in Dynamic Social Networks

Authors:

Peizhi Wu,

Yi Tu,

Zhenglu Yang,

Adam Jatowt,

Masato OdagakiAuthors Info & Claims

WWW '18: Companion Proceedings of the The Web Conference 2018

Pages 115 - 116

https://doi.org/10.1145/3184558.3186956

Published: 23 April 2018 Publication History

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Abstract

Modeling the evolution of user preferences and item attributes in a dynamic social network is important because it is the basis for many applications, including recommendation systems and user behavior analysis. This study introduces a comprehensive general neural framework with several optimal strategies to jointly model the evolution of user preferences and item attributes in dynamic social networks. Preliminary experimental results conducted on real-world datasets demonstrate that our model performs better than the state-of-the-art methods.

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Cited By

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Wang YHuang XMa JJin Q(2024)LASGRec: A Personalized Recommender Based on Learnable Attribute Sampling and Graph Neural NetworkIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.331143311:2(2930-2939)Online publication date: Apr-2024
https://doi.org/10.1109/TCSS.2023.3311433
Kontogianni AAlepis EVirvou MPatsakis CKontogianni AAlepis EVirvou MPatsakis C(2024)Implementing Machine Learning for Smart Tourism FrameworksSmart Tourism–The Impact of Artificial Intelligence and Blockchain10.1007/978-3-031-50883-7_6(87-120)Online publication date: 2-Feb-2024
https://doi.org/10.1007/978-3-031-50883-7_6
Zhang ZLiu QJiang HWang FZhuang YWu LGao WChen EOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)FairLISAProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667918(41432-41450)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3667918
Show More Cited By

Index Terms

Deep Modeling of the Evolution of User Preferences and Item Attributes in Dynamic Social Networks
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Social recommendation
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. World Wide Web
    1. Web searching and information discovery
      1. Collaborative filtering
      2. Social recommendation

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Information & Contributors

Information

Published In

WWW '18: Companion Proceedings of the The Web Conference 2018

April 2018

2023 pages

ISBN:9781450356404

General Chairs:
Pierre-Antoine Champin
Université Claude Bernard Lyon 1, France
,
Fabien Gandon
Inria, Université Côte d'Azur, CNRS, I3S, France
,
Lionel Médini
Université Claude Bernard Lyon 1, CNRS, LIRIS, France
,
Program Chairs:
Mounia Lalmas
Spotify, UK
,
Panagiotis G. Ipeirotis
New York University, USA

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]

In-Cooperation

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

Publisher

International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 23 April 2018

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Poster

Funding Sources

Research Fund for International Young Scientists
National Natural Science Foundation of China

Conference

WWW '18

Sponsor:

IW3C2

WWW '18: The Web Conference 2018

April 23 - 27, 2018

Lyon, France

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

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Citations

Cited By

View all

Wang YHuang XMa JJin Q(2024)LASGRec: A Personalized Recommender Based on Learnable Attribute Sampling and Graph Neural NetworkIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.331143311:2(2930-2939)Online publication date: Apr-2024
https://doi.org/10.1109/TCSS.2023.3311433
Kontogianni AAlepis EVirvou MPatsakis CKontogianni AAlepis EVirvou MPatsakis C(2024)Implementing Machine Learning for Smart Tourism FrameworksSmart Tourism–The Impact of Artificial Intelligence and Blockchain10.1007/978-3-031-50883-7_6(87-120)Online publication date: 2-Feb-2024
https://doi.org/10.1007/978-3-031-50883-7_6
Zhang ZLiu QJiang HWang FZhuang YWu LGao WChen EOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)FairLISAProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667918(41432-41450)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3667918
Liu QWu JHuang ZWang HNing YChen MChen EYi JZhou B(2023)Federated User Modeling from Hierarchical InformationACM Transactions on Information Systems10.1145/356048541:2(1-33)Online publication date: 3-Apr-2023
https://dl.acm.org/doi/10.1145/3560485
Wu LHe XWang XZhang KWang M(2023)A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.314569035:5(4425-4445)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1109/TKDE.2022.3145690
Kontogianni AAlepis E(2023)Social Network Data Enabling Smart Tourism2023 14th International Conference on Information, Intelligence, Systems & Applications (IISA)10.1109/IISA59645.2023.10345898(1-6)Online publication date: 10-Jul-2023
https://doi.org/10.1109/IISA59645.2023.10345898
Wu JLiu QHuang ZNing YWang HChen EYi JZhou B(2021)Hierarchical Personalized Federated Learning for User ModelingProceedings of the Web Conference 202110.1145/3442381.3449926(957-968)Online publication date: 19-Apr-2021
https://dl.acm.org/doi/10.1145/3442381.3449926
Yoon J(2018)Trustworthiness of Dynamic Moving Sensors for Secure Mobile Edge ComputingComputers10.3390/computers70400637:4(63)Online publication date: 16-Nov-2018
https://doi.org/10.3390/computers7040063

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Improving Cold Start Recommendation by Mapping Feature-Based Preferences to Item Comparisons

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