Researchers have developed a new framework for online influence maximization that accounts for the total cost of an advertising campaign, rather than just the number of influencers. This approach is more realistic for real-world scenarios where influencer costs differ and advertisers aim to maximize value within a set budget. The proposed algorithm, designed for an independent cascade diffusion model with semi-bandit feedback, includes theoretical and experimental validation, also improving existing regret bounds for cardinality-constrained settings. AI
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IMPACT Introduces a more realistic budgeting model for influence maximization, potentially improving ad campaign efficiency.
RANK_REASON Academic paper introducing a new algorithmic framework for influence maximization.
