short-paper

On bundle configuration for viral marketing in social networks

Authors:

Wang-Chien Lee,

Hui-Ju HungAuthors Info & Claims

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

Pages 2234 - 2238

https://doi.org/10.1145/2396761.2398608

Published: 29 October 2012 Publication History

Abstract

Prior research on viral marketing mostly focuses on promoting one single product item. In this work, we explore the idea of bundling multiple items for viral marketing and formulate a new research problem, called Bundle Configuration for SpreAd Maximization (BCSAM). Efficiently obtaining an optimal product bundle under the setting of BCSAM is very challenging. Aiming to strike a balance between the quality of solution and the computational overhead, we systematically explore various heuristics to develop a suite of algorithms, including κ-Bundle Configuration and Aggregated Bundle Configuration. Moreover, we integrate all the proposed ideas into one efficient algorithm, called Aggregated Bundle Configuration (ABC). Finally, we conduct an extensive performance evaluation on our proposals. Experimental results show that ABC significantly outperforms its counterpart and two baseline approaches in terms of both computational overhead and bundle quality.

References

[1]

Business insider. P&G to lay off 1,600 after discovering it's free to advertise on facebook. http://articles.businessinsider.com/2012-01-30/news/31004736_1_advertising-digital-media-procter-gamble.

[2]

M. Agarwal and S. Chatterjee. Complexity, uniqueness, and similarity in between-bundle choice. Journal of Product and Brand Management, 2003.

[3]

W. Chen, C. Wang, and Y. Wang. Scalable influence maximization for prevalent viral marketing in large-scale social networks. In KDD, 2010.

Digital Library

[4]

S. Datta, A. Majumder, and N. Shrivastava. Viral marketing for multiple products. In ICDM, 2010.

Digital Library

[5]

X. Geng, M. B. Stinchcombe, and A. B. Whinston. Bundling information goods of decreasing value. Management Science, 2005.

Digital Library

[6]

K.-Y. Goh, C. S. Lee, and C. H. Lee. Information technology product bundling in the presence of complementarities, quality uncertainty, and network effects: An agent-based approach. In ICIS, 2003.

[7]

A. Goyal, F. Bonchi, and L. V. S. Lakshmanan. A data-based approach to social influence maximization. In VLDB, 2011.

Digital Library

[8]

J. Harris and E. A. Blair. Consumer preference for product bundles: The role of reduced search costs. Journal of the Academy of Marketing Science, 2006.

[9]

D. Kempe, J. M. Kleinberg, and É. Tardos. Maximizing the spread of influence through a social network. In KDD, 2003.

Digital Library

[10]

I. R. Misner. The World's best known marketing secret: Building your business with word-of-mouth marketing. Bard Press, 2nd edition, 1999.

[11]

R. B. Myerson and M. A. Satterthwaite. Efficient mechanisms for bilateral trading. Journal of Economic Theory, 1983.

[12]

J. Nail. The consumer advertising backlash, 2004. Forrester Research and Intelliseek Market Research Report.

[13]

R. L. Schmalenesee. Gaussian demand and commodity bundling. Journal of Business, 1984.

[14]

D. Trpevski, W. K. S. Tang, and L. Kocarev. Model for rumor spreading over networks. Physical Review E, 2010.

[15]

M. Ye, P. Yin, W.-C. Lee, and D. L. Lee. Exploiting geographical influence for collaborative point-of-interest recommendation. In SIGIR, 2011.

Digital Library

Cited By

Sun ZFeng KYang JQu XFang HOng YLiu WHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Adaptive In-Context Learning with Large Language Models for Bundle GenerationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657808(966-976)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657808
Tzaban HGuy IGreenstein-Messica ADagan ARokach LShapira BHuang JChang YCheng XKamps JMurdock VWen JLiu Y(2020)Product Bundle Identification using Semi-Supervised LearningProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401128(791-800)Online publication date: 25-Jul-2020
https://dl.acm.org/doi/10.1145/3397271.3401128
Yeh LWu H(2020)Efficient Extraction of Target Users for Package Promotion in Big Social NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2020.30077547:5(1111-1122)Online publication date: Oct-2020
https://doi.org/10.1109/TCSS.2020.3007754
Show More Cited By

Index Terms

On bundle configuration for viral marketing in social networks
1. Theory of computation
  1. Design and analysis of algorithms

Recommendations

Identification of multi-spreader users in social networks for viral marketing

Identifying high spreading power nodes is an interesting problem in social networks. Finding super spreader nodes becomes an arduous task when the nodes appear in large numbers, and the number of existing links becomes enormous among them. One of the ...
Effective Method for Promoting Viral Marketing in Microblog
SOCIALCOM '13: Proceedings of the 2013 International Conference on Social Computing

Based on word-of-mouth effect, viral marketing has developed into an important marketing strategy recently. Meanwhile, microblog has drawn global attention as a potential marketing group over the past decade. Thus how to promote viral marketing in ...
A Seeding Cellular Learning Automata Approach for Viral Marketing In Social Network
iiWAS '14: Proceedings of the 16th International Conference on Information Integration and Web-based Applications & Services

Viral Marketing is a marketing technique that uses social network to get customers to market an idea, product, or service on their own by spreading the influence among their followers. The customers who are responsible of the information spreading are ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

October 2012

2840 pages

ISBN:9781450311564

DOI:10.1145/2396761

General Chair:
Xuewen Chen
Wayne State University, USA
,
Program Chairs:
Guy Lebanon
Georgia Institute of Technology
,
Haixun Wang
Microsoft Research Asia
,
Mohammed J. Zaki
Rensselaer Polytechnic Institute

Copyright © 2012 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

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 October 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Conference

CIKM'12

Sponsor:

CIKM'12: 21st ACM International Conference on Information and Knowledge Management

October 29 - November 2, 2012

Hawaii, Maui, USA

Acceptance Rates

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
426
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)0

Reflects downloads up to 15 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Sun ZFeng KYang JQu XFang HOng YLiu WHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Adaptive In-Context Learning with Large Language Models for Bundle GenerationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657808(966-976)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657808
Tzaban HGuy IGreenstein-Messica ADagan ARokach LShapira BHuang JChang YCheng XKamps JMurdock VWen JLiu Y(2020)Product Bundle Identification using Semi-Supervised LearningProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401128(791-800)Online publication date: 25-Jul-2020
https://dl.acm.org/doi/10.1145/3397271.3401128
Yeh LWu H(2020)Efficient Extraction of Target Users for Package Promotion in Big Social NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2020.30077547:5(1111-1122)Online publication date: Oct-2020
https://doi.org/10.1109/TCSS.2020.3007754
Ma LCao GKaplan L(2017)Graphical approach for influence maximization in social networks under generic threshold-based non-submodular model2017 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2017.8258017(970-975)Online publication date: Dec-2017
https://doi.org/10.1109/BigData.2017.8258017
Huang JZhou YJia XSun H(2016)A Novel Social Event Organization Approach for Diverse User ChoicesThe Computer Journal10.1093/comjnl/bxw059Online publication date: 8-Sep-2016
https://doi.org/10.1093/comjnl/bxw059
Shuai HYang DYu PChen M(2013)Willingness optimization for social group activityProceedings of the VLDB Endowment10.14778/2732240.27322447:4(253-264)Online publication date: 1-Dec-2013
https://dl.acm.org/doi/10.14778/2732240.2732244
Cui PJin SYu LWang FZhu WYang SGrossman RUthurusamy RDhillon IKoren Y(2013)Cascading outbreak prediction in networksProceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/2487575.2487639(901-909)Online publication date: 11-Aug-2013
https://dl.acm.org/doi/10.1145/2487575.2487639
Dai BLauw H(2013)Modeling Preferences with Availability Constraints2013 IEEE 13th International Conference on Data Mining10.1109/ICDM.2013.41(101-110)Online publication date: Dec-2013
https://doi.org/10.1109/ICDM.2013.41

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents