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[DdT19] RTIM: a Real-Time Influence Maximization Strategy

Conférence Internationale avec comité de lecture : International Conference on Web Information Systems Engineering (WISE), November 2019, pp.1-15, Hong-Kong,
motcle:
Résumé: Influence Maximization (IM) is a well studied maximum coverage problem, which consists in finding the top-k influencers in a network who will maximize the diffusion of a piece of information. It is commonly associated with basic online advertising strategies. However, today, the exponential growth of online advertising is due to Real-Time Bidding (RTB) which allows advertising agencies to target specific users with specific ads on specific webpages. It requires complex ad placement decisions, made in real-time to face a high-speed stream of online users. In order to stay relevant, the IM problem should be updated to answer RTB needs. While most traditional IM methods generate a static set of top-k influencers, they do not deal with the topic of influence maximization in a real-time bidding environment which requires dynamic influence targeting. This paper studies this fascinating topic and proposes RTIM the first IM algorithm capable of taking influence maximization decisions within a real-time bidding environment.We also provide a deep analysis of users’ influence scores for various social networks. Finally, we offer a thorough experimental process to compare static versus dyanmic IM solutions.

BibTeX

@inproceedings {
DdT19,
title="{RTIM: a Real-Time Influence Maximization Strategy}",
author=" D. Dupuis and C. du Mouza and N. Travers ",
booktitle="{International Conference on Web Information Systems Engineering (WISE)}",
year=2019,
month="November",
pages="1-15",
address=" Hong-Kong",
}