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Constrained Social Community Recommendation

Authors:

Sibo WangAuthors Info & Claims

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages 5586 - 5596

https://doi.org/10.1145/3580305.3599793

Published: 04 August 2023 Publication History

Abstract

In online social networks, users with similar interests tend to come together, forming social communities. Nowadays, user-defined communities become a prominent part of online social platforms as people who have joined such communities tend to be more active in social networks. Therefore, recommending explicit communities to users provides great potential to advance online services.

In this paper, we focus on the constrained social community recommendation problem in real applications, where each user can only join at most one community. Previous attempts at community recommendation mostly adopt collaborative filtering approaches or random walk-based approaches, while ignoring social relationships between users as well as the local structure of each community. Therefore, they only derive an extremely sparse affinity matrix, which degrades the model performances. To tackle this issue, we propose ComRec which simultaneously captures both global and local information on the extended graph during pre-computation, speeding up the training process on real-world large graphs. In addition, we present a labeling component to improve the expressiveness of our framework. We conduct experiments on three Tencent mobile games to evaluate our proposed method. Extensive experimental results show that our ComRec consistently outperforms other competitors by up to 12.80% and 6.61% in the corresponding evaluation metrics of offline and online experiments, respectively.

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Cited By

Zou TMao YYe JDu BBaeza-Yates RBonchi F(2024)Repeat-Aware Neighbor Sampling for Dynamic Graph LearningProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672001(4722-4733)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672001
Huang XLin DHuang WSun SWen JChen C(2024)PlatoD2GL: An Efficient Dynamic Deep Graph Learning System for Graph Neural Network Training on Billion-Scale Graphs2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00191(2421-2434)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00191
Wang ZChen JLi JWang Z(2024)Interest community-based recommendation via cognitive similarity and adaptive evolutionary clusteringChaos, Solitons & Fractals10.1016/j.chaos.2024.115085185(115085)Online publication date: Aug-2024
https://doi.org/10.1016/j.chaos.2024.115085
Show More Cited By

Index Terms

Constrained Social Community Recommendation
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Learning latent representations
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. World Wide Web
    1. Web applications
      1. Social networks
    2. Web searching and information discovery
      1. Social recommendation

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Published In

cover image ACM Conferences

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 2023

5996 pages

ISBN:9798400701030

DOI:10.1145/3580305

General Chairs:
Ambuj Singh
UC Santa Barbara, USA
,
Yizhou Sun
UC Los Angeles, USA
,
Program Chairs:
Leman Akoglu
Carnegie Mellon University, USA
,
Dimitrios Gunopulos
University of Athens, Greece
,
Xifeng Yan
UC Santa Barbara, USA
,
Ravi Kumar
Google, USA
,
Fatma Ozcan
Google, USA
,
Jieping Ye
Alibaba DAMO Academy

Copyright © 2023 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 the author(s) 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].

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Publication History

Published: 04 August 2023

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Hong Kong ITC ITF
Hong Kong RGC GRF
Hong Kong RGC CRF
National Natural Science Foundation of China
CUHK Direct Grant
Hong Kong RGC ECS

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KDD '23

Sponsor:

KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 6 - 10, 2023

CA, Long Beach, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Zou TMao YYe JDu BBaeza-Yates RBonchi F(2024)Repeat-Aware Neighbor Sampling for Dynamic Graph LearningProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672001(4722-4733)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672001
Huang XLin DHuang WSun SWen JChen C(2024)PlatoD2GL: An Efficient Dynamic Deep Graph Learning System for Graph Neural Network Training on Billion-Scale Graphs2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00191(2421-2434)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00191
Wang ZChen JLi JWang Z(2024)Interest community-based recommendation via cognitive similarity and adaptive evolutionary clusteringChaos, Solitons & Fractals10.1016/j.chaos.2024.115085185(115085)Online publication date: Aug-2024
https://doi.org/10.1016/j.chaos.2024.115085
Shan ZZhang DLei Y(2024)Graph contrastive learning with cross-encoder for community discoveryApplied Intelligence10.1007/s10489-024-05287-354:2(2211-2224)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1007/s10489-024-05287-3
Zhang XHuang JZhang FWang S(2024)FICOM: an effective and scalable active learning framework for GNNs on semi-supervised node classificationThe VLDB Journal10.1007/s00778-024-00870-z33:5(1723-1742)Online publication date: 22-Jul-2024
https://doi.org/10.1007/s00778-024-00870-z
Hao PQian ZWang SBai C(2023)Community aware graph embedding learning for item recommendationWorld Wide Web10.1007/s11280-023-01224-526:6(4093-4108)Online publication date: 7-Dec-2023
https://dl.acm.org/doi/10.1007/s11280-023-01224-5

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