The problem of influence maximization is to select a small set of seed users in a social network to maximize the spread of influence. Recently, this problem has attracted considerable attention due to its applications in both commercial and social fields, such as product promotion, contagion prevention and public opinion forecasting. Most of prior work focuses on the diffusion model of single propagating entity, purely-complementary entities or purely-competitive entities. However, in reality, the influence diffusion in the social network is certainly more general, involving multiple propagating entities, which are competitive or complementary rather than single entity, purely-complementary entities or purely-competitive entities.

In this paper, we consider the problem that a company (follower) intends to promote a new product into the market by maximizing the influence of a social network, where multiple competitive and complementary products have been spreading We propose a Competitive and Complementary Independent Cascade (CCIC) diffusion model, and propose a novel optimization problem, follower-based influence maximization that aims to select top-K influential nodes as seed nodes, which can maximize the influence of a social network where multiple competitive and complementary products have already been propagated. To solve follower-based influence maximization problem, we propose a Deep Recursive Hybrid model (DRH) and an approximation algorithm (DRHGA). The DRH model dynamically tracks entity correlations, cascade correlations, causalities between ratings and next-period adoption through a deep recursive network and compute influence probabilities between nodes on target product. Then, with the influence probabilities predicted through DRH model, the DRHGA algorithm can efficiently find the seed node set for the target product under the CCIC diffusion model. Experiments on several public datasets have been conducted, and experimental results show that our method outperforms the state-of-the-art methods on prediction accuracy and efficiency.

影响力最大化的问题是在社交网络中选择一小组种子用户以最大化影响力的传播。近来，由于其在商业和社会领域中的应用，例如产品促销，传染病预防和舆论预测，该问题已引起相当大的关注。先前的大部分工作都集中在单个传播实体，纯互补实体或纯竞争实体的扩散模型上。但是，实际上，社交网络中的影响力扩散肯定更为普遍，涉及多个传播实体，它们是竞争性的或互补的，而不是单个实体，纯粹的互补性实体或纯粹的竞争性实体。

在本文中，我们考虑了公司（追随者）打算通过最大化社交网络的影响力将新产品推向市场的问题，在该社交网络中，多个竞争性和互补性产品已经扩散。我们提出了竞争性和互补性独立级联（ CCIC）扩散模型，并提出了一个新的优化问题，即基于跟随者的影响最大化，旨在选择排名前K的有影响力的节点作为种子节点，从而可以最大化已经传播了多个竞争和互补产品的社交网络的影响力。为了解决基于跟随者的影响最大化问题，我们提出了一种深度递归混合模型（DRH）和一种近似算法（DRHGA）。DRH模型动态跟踪实体相关性，级联相关性，通过深度递归网络在评级和下一周期采用之间的因果关系，并计算目标产品上节点之间的影响概率。然后，利用DRH模型预测的影响概率，DRHGA算法可以有效地找到CCIC扩散模型下目标产品的种子节点集。在几个公共数据集上进行了实验，实验结果表明，我们的方法在预测准确性和效率上都优于最新方法。