论文标题
社交网络中自适应多功能预算的利润最大化
Adaptive Multi-Feature Budgeted Profit Maximization in Social Networks
论文作者
论文摘要
在线社交网络一直是病毒营销的最重要平台之一。关于在网络上采用新产品的扩散的大多数现有研究大约是一个扩散。也就是说，有关该产品的信息仅在网络上传播。但是，实际上，一种产品可能具有多个功能，有关不同功能的信息可能会在社交网络中独立传播。当用户想购买产品时，他会全面考虑产品的所有功能，而不仅仅是考虑一个。基于这一点，我们提出了一个新的问题，多功能预算的利润最大化（MBPM）问题，该问题首先考虑了预算的利润最大化，在一种产品的多个特征传播下。 鉴于一个社交网络，每个节点都有激活成本和利润，MBPM问题寻求的种子设置的预期成本不超过预算，以使总预期利润尽可能大。我们考虑在自适应设置下MBPM问题，其中选择种子，并根据当前的扩散结果选择下一个种子。我们在两个模型下研究自适应MBPM问题，分别是Oracle模型和噪声模型。 Oracle模型假设可以在O（1）时间中获得任何节点的有条件预期的边际利润，并提出了A（1-1/e）预期的近似策略。在噪声模型下，我们通过修改Epic算法并提出有效的策略来估计节点的条件预期利润，该策略可以返回（1-EXP（ε-1））预期的近似值。在六个现实的数据集上进行了几项实验，以将我们提出的政策与相应的非自适应算法和一些启发式适应性策略进行比较。实验结果表明我们政策的效率和优势。
Online social network has been one of the most important platforms for viral marketing. Most of existing researches about diffusion of adoptions of new products on networks are about one diffusion. That is, only one piece of information about the product is spread on the network. However, in fact, one product may have multiple features and the information about different features may spread independently in social network. When a user would like to purchase the product, he would consider all of the features of the product comprehensively not just consider one. Based on this, we propose a novel problem, multi-feature budgeted profit maximization (MBPM) problem, which first considers budgeted profit maximization under multiple features propagation of one product. Given a social network with each node having an activation cost and a profit, MBPM problem seeks for a seed set with expected cost no more than the budget to make the total expected profit as large as possible. We consider MBPM problem under the adaptive setting, where seeds are chosen iteratively and next seed is selected according to current diffusion results. We study adaptive MBPM problem under two models, oracle model and noise model. The oracle model assumes conditional expected marginal profit of any node could be obtained in O(1) time and a (1-1/e) expected approximation policy is proposed. Under the noise model, we estimate conditional expected marginal profit of a node by modifying the EPIC algorithm and propose an efficient policy, which could return a (1-exp(ε-1)) expected approximation ratio. Several experiments are conducted on six realistic datasets to compare our proposed policies with their corresponding non-adaptive algorithms and some heuristic adaptive policies. Experimental results show efficiencies and superiorities of our policies.
