考虑广告总成本的在线影响力最大化新框架，更贴近真实营销场景。
Budgeted Online Influence Maximization
- 以总预算替代选人数量限制，更符合实际广告投入需求。
- 在独立级联模型下实现低后悔率，理论与实验均验证有效性。
- 适用于预算有限的社交广告投放，尤其适合资源敏感型决策者。
我们提出一种新的预算约束在线影响力最大化框架，将广告活动的总成本作为核心考量，而非传统的人选数量上限。该方法更真实地反映了现实中不同影响者成本差异的情况，帮助广告商在有限预算内获得最优性价比。我们设计了一种算法，假设采用独立级联扩散模型，并基于边级别半-强化反馈机制。理论分析表明，该方法在预算约束和经典基数约束两种情形下均成立，且在后者中改进了现有最优后悔界。实验结果验证了算法的有效性与稳定性。
原文摘要 · Abstract (English)
We introduce a new budgeted framework for online influence maximization, considering the total cost of an advertising campaign instead of the common cardinality constraint on a chosen influencer set. Our approach better models the real-world setting where the cost of influencers varies and advertisers want to find the best value for their overall social advertising budget. We propose an algorithm assuming an independent cascade diffusion model and edge level semi-bandit feedback, and provide both theoretical and experimental results. Our analysis is also valid for the cardinality constraint setting and improves the state of the art regret bound in this case.
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