RTIM: A Real-Time Influence Maximization Strategy

Dupuis, David; Mouza, Cédric Du; Travers, Nicolas; Chareyron, Gaël

doi:10.1007/978-3-030-34223-4_18

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…RTB is an online auction system which allows advertisers to bid in real time for ad locations on a Web page loaded by users and thus to target them efficiently. In RTB, advertisers see a stream of users, one at a time and have less than 100 ms [11,38] to decide to bid or not. The bidders do not know what the auction landscape looks like and consequently cannot oversee future connections.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Influence Maximization in a RTB Setting

et al. 2020

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To maximize the impact of an advertisement campaign on social networks, the real-time bidding (RTB) systems aim at targeting the most influential users of this network. Influence maximization (IM) is a solution that addresses this issue by maximizing the coverage of the network with top-k influencers who maximize the diffusion of information. Associated with online advertising strategies at Web scale, RTB is faced with complex ad placement decisions in real time to deal with a high-speed stream of online users. To tackle this issue, IM strategies should be modified in order to integrate RTB constraints. While most traditional IM methods deal with static sets of top influencers, they hardly address the dynamic influence targeting issue by integrating short time decision, no interchange and stream's incompleteness. This paper proposes a real-time influence maximization approach which takes influence maximization decisions within a real-time bidding environment. A deep analysis of influence scores of users over several social networks is presented as well a strategy to guarantee the impact of an IM strategy in order to define the budget of an ad campaign. Finally, we offer a thorough experimental process to compare static versus dynamic IM solutions wrt. influence scores.

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Section: Introductionmentioning

confidence: 99%

Real-Time Influence Maximization in a RTB Setting

et al. 2020

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“…In these networks, the most popular and inuential users have quickly been the center of attention for many applications, since they will accelerate the spread of information to the greatest number of users [8]. For instance, for online advertising campaigns on social networks or on the Web, advertisers seek to place their advertisements among the users who have the most visibility in order to reach a maximum of people [2,5,9]. Likewise, for marketing purposes, highly followed users, called inuencers, are paid to test and promote dierent products.…”

Section: Introductionmentioning

confidence: 99%

A pattern-based approach for an early detection of popular Twitter accounts

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Brunessaux

Constantin

et al. 2020

Proceedings of the 24th Symposium on International Database Engineering &Amp; Applications

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Social networks (SN) are omnipresent in our lives today. Not all users have the same behaviour on these networks. If some have a low activity, rarely posting messages and following few users, some others at the other extreme have a signicant activity, with many followers and regularly posts. The important role of these popular SN users makes them the target of many applications for example for content monitoring or advertising. It is therefore relevant to be able to predict as soon as possible which SN users will become popular. In this work, we propose a technique for early detection of such users based on the identication of characteristic patterns. We present an index, 2 ", which allows a scaling up of our approach to large social networks. We also describe our rst experiments that conrm the validity of our approach. CCS CONCEPTS • Information systems ! Social networks.

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RTIM: A Real-Time Influence Maximization Strategy

Cited by 2 publications

References 27 publications

Real-Time Influence Maximization in a RTB Setting

Real-Time Influence Maximization in a RTB Setting

A pattern-based approach for an early detection of popular Twitter accounts

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