多轮社交广告序列影响最大化
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浙江大学

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TP

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国家自然科学基金项目(面上项目,重点项目,重大项目);浙江大学上海高等研究院繁星科学基金(SN-ZJU-SIAS-001)


Multi-round Social Advertising Sequence Influence Maximization
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    摘要:

    现有的序列广告推荐研究主要关注用户对广告的偏好,未充分考虑广告间的正向关系。从广告间的关联出发,将广告网络和用户网络同时纳入考量,构建了基于触发模型的多轮广告序列推荐影响力最大化模型。提出了基于广告边的多轮反向影响力采样贪心策略,以提升广告平台收益,并证明了这一方法具有严格的理论下界保证。实验表明,与现有最优方法相比,该方法的广告传播影响力收益平均提升了35%,显著增强了广告推荐效果,为广告序列推荐提供了新的解决方案。

    Abstract:

    Existing research on sequential ad recommendations mainly focuses on user preferences for ads, insufficiently considering positive relationships between ads. Starting from the associations between ads, incorporates both ad networks and user networks into consideration, a multi-round social advertising influence maximization model based on triggering model was constructed. An ad edge based greedy strategy based on multi-round reverse influence sampling was proposed to enhance platform revenue, with theoretical proofs of its strict lower bound guarantee. Experiments showed that compared to existing optimal methods, this method increased the average ad propagation influence revenue by 35%, significantly enhancing ad recommendation effectiveness, providing a new solution for ad sequence recommendations.

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  • 收稿日期:2024-10-30
  • 最后修改日期:2025-03-18
  • 录用日期:2025-03-19
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