On the Application of Personalization Techniques to News Servers on the WWW
AI*IA '99 Proceedings of the 6th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Social Networks for Targeted Advertising
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 06
AdROSA-Adaptive personalization of web advertising
Information Sciences: an International Journal
Optimal marketing strategies over social networks
Proceedings of the 17th international conference on World Wide Web
Yes, there is a correlation: - from social networks to personal behavior on the web
Proceedings of the 17th international conference on World Wide Web
Influence and correlation in social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Combining Behavioral and Social Network Data for Online Advertising
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
A measurement-driven analysis of information propagation in the flickr social network
Proceedings of the 18th international conference on World wide web
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Extracting influential nodes for information diffusion on a social network
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
A Study of Information Diffusion over a Realistic Social Network Model
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Extracting influential nodes on a social network for information diffusion
Data Mining and Knowledge Discovery
Scalable influence maximization for prevalent viral marketing in large-scale social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Optimization-based influencing of village social networks in a counterinsurgency
SBP'11 Proceedings of the 4th international conference on Social computing, behavioral-cultural modeling and prediction
Leveraging Network Properties for Trust Evaluation in Multi-agent Systems
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Influential nodes in a diffusion model for social networks
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
Behavioral analyses of information diffusion models by observed data of social network
SBP'10 Proceedings of the Third international conference on Social Computing, Behavioral Modeling, and Prediction
Hierarchical influence maximization for advertising in multi-agent markets
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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The question of how to influence people in a large social system is a perennial problem in marketing, politics, and publishing. It differs from more personal inter-agent interactions that occur in negotiation and argumentation since network structure and group membership often pay a more significant role than the content of what is being said, making the messenger more important than the message. In this paper, we propose a new method for propagating information through a social system and demonstrate how it can be used to develop a product advertisement strategy in a simulated market. We consider the desire of agents toward purchasing an item as a random variable and solve the influence maximization problem in steady state using an optimization method to assign the advertisement of available products to appropriate messenger agents. Our market simulation accounts for the 1) effects of group membership on agent attitudes 2) has a network structure that is similar to realistic human systems 3) models inter-product preference correlations that can be learned from market data. The results show that our method is significantly better than network analysis methods based on centrality measures.