research-article

When Social Influence Meets Item Inference

Authors:

Hong-Han Shuai,

Liang-Hao Huang,

Wang-Chien Lee,

Ming-Syan ChenAuthors Info & Claims

KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 915 - 924

https://doi.org/10.1145/2939672.2939758

Published: 13 August 2016 Publication History

Abstract

Research issues and data mining techniques for product recommendation and viral marketing have been widely studied. Existing works on seed selection in social networks do not take into account the effect of product recommendations in e-commerce stores. In this paper, we investigate the seed selection problem for viral marketing that considers both effects of social influence and item inference (for product recommendation). We develop a new model, Social Item Graph (SIG), that captures both effects in the form of hyperedges. Accordingly, we formulate a seed selection problem, called Social Item Maximization Problem (SIMP), and prove the hardness of SIMP. We design an efficient algorithm with performance guarantee, called Hyperedge-Aware Greedy (HAG), for SIMP and develop a new index structure, called SIG-index, to accelerate the computation of diffusion process in HAG. Moreover, to construct realistic SIG models for SIMP, we develop a statistical inference based framework to learn the weights of hyperedges from data. Finally, we perform a comprehensive evaluation on our proposals with various baselines. Experimental result validates our ideas and demonstrates the effectiveness and efficiency of the proposed model and algorithms over baselines.

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Cited By

Teng YShi YYang DLee WYu PLu YChen M(2022)Epidemic Spread Optimization for Disease Containment with NPIs and Vaccination2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00258(2845-2858)Online publication date: May-2022
https://doi.org/10.1109/ICDE53745.2022.00258
Teng YShi YTai CYang DLee WChen M(2021)Influence Maximization Based on Dynamic Personal Perception in Knowledge Graph2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00132(1488-1499)Online publication date: Apr-2021
https://doi.org/10.1109/ICDE51399.2021.00132
Cao QShen HGao JCheng X(2021)Learning diffusion model-free and efficient influence function for influence maximization from information cascadesKnowledge and Information Systems10.1007/s10115-021-01556-6Online publication date: 19-Mar-2021
https://doi.org/10.1007/s10115-021-01556-6
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Index Terms

When Social Influence Meets Item Inference
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Social networks
2. Information systems
  1. World Wide Web
    1. Online advertising
      1. Social advertising

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Published In

cover image ACM Conferences

KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2016

2176 pages

ISBN:9781450342322

DOI:10.1145/2939672

General Chairs:
Balaji Krishnapuram
IBM
,
Mohak Shah
Bosch
,
Program Chairs:
Alex Smola
Amazon
,
Charu Aggarwal
IBM
,
Dou Shen
Baidu
,
Rajeev Rastogi
Amazon

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]

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Publication History

Published: 13 August 2016

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KDD '16

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KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 13 - 17, 2016

California, San Francisco, USA

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KDD '16 Paper Acceptance Rate 66 of 1,115 submissions, 6%;

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

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Cited By

Teng YShi YYang DLee WYu PLu YChen M(2022)Epidemic Spread Optimization for Disease Containment with NPIs and Vaccination2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00258(2845-2858)Online publication date: May-2022
https://doi.org/10.1109/ICDE53745.2022.00258
Teng YShi YTai CYang DLee WChen M(2021)Influence Maximization Based on Dynamic Personal Perception in Knowledge Graph2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00132(1488-1499)Online publication date: Apr-2021
https://doi.org/10.1109/ICDE51399.2021.00132
Cao QShen HGao JCheng X(2021)Learning diffusion model-free and efficient influence function for influence maximization from information cascadesKnowledge and Information Systems10.1007/s10115-021-01556-6Online publication date: 19-Mar-2021
https://doi.org/10.1007/s10115-021-01556-6
Kankeu Fotsing CTeng YChiang SChen YHsu B(2021)Activity Organization Queries for Location-Aware Heterogeneous Information NetworkDatabase Systems for Advanced Applications. DASFAA 2021 International Workshops10.1007/978-3-030-73216-5_20(283-304)Online publication date: 6-Apr-2021
https://doi.org/10.1007/978-3-030-73216-5_20
Wang JWang HZhao MCao JLi ZGuo M(2020)Joint Topic-Semantic-aware Social Matrix Factorization for online voting recommendationKnowledge-Based Systems10.1016/j.knosys.2020.106433210(106433)Online publication date: Dec-2020
https://doi.org/10.1016/j.knosys.2020.106433
Zheng HWang NWu J(2019)Non-Submodularity and Approximability: Influence Maximization in Online Social Networks2019 IEEE 20th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)10.1109/WoWMoM.2019.8793017(1-9)Online publication date: Jun-2019
https://doi.org/10.1109/WoWMoM.2019.8793017
Zhu JZhu JGhosh SWu WYuan J(2019)Social Influence Maximization in Hypergraph in Social NetworksIEEE Transactions on Network Science and Engineering10.1109/TNSE.2018.28737596:4(801-811)Online publication date: 1-Oct-2019
https://doi.org/10.1109/TNSE.2018.2873759
Zhu JGhosh SWu W(2019)Group Influence Maximization Problem in Social NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2019.29385756:6(1156-1164)Online publication date: Dec-2019
https://doi.org/10.1109/TCSS.2019.2938575
Oida KOkubo K(2019)Adopter Community Formation Accelerated by Repeaters of Product Advertisement CampaignsIEEE Transactions on Computational Social Systems10.1109/TCSS.2018.28836146:1(56-72)Online publication date: Feb-2019
https://doi.org/10.1109/TCSS.2018.2883614
Chang TShi YYang DChen W(2019)Seed Selection and Social Coupon Allocation for Redemption Maximization in Online Social Networks2019 IEEE 35th International Conference on Data Engineering (ICDE)10.1109/ICDE.2019.00044(410-421)Online publication date: Apr-2019
https://doi.org/10.1109/ICDE.2019.00044
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