Marketing in the social network environment integrates current advanced internet and information technologies. This marketing method not only broadens marketing channels and builds a network communication platform but also meets the purchase needs of customers in the entire market and shortens customer purchases. The process is also an inevitable product of the development of the times. However, when companies use social networks for product marketing, they usually face the impact of multiple realistic factors. This article takes the maximization of influence as the main idea to find seed users for product information dissemination and also considers the users’ interest preferences. The target users can influence the product, and the company should control marketing costs to obtain a larger marginal benefit. Based on this, this paper considers factors such as the scale of information diffusion, user interest preferences, and corporate budgets, takes the influence maximization model as a multiobjective optimization problem, and proposes a multiobjective maximization of influence (MOIM) model. To solve the NP-hard problem of maximizing influence, this paper uses Monte Carlo sampling to calculate high-influence users. Next, a seed user selection algorithm based on NSGA-II is proposed to optimize the above three objective functions and find the optimal solution. We use real social network data to verify the performance of models and methods. Experiments show that the proposed model can generate appropriate seed sets and can meet different purposes of information dissemination. Sensitivity analysis proves that our model is robust under different actual conditions.

中文翻译：

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基于用户社交网络信息的营销渠道优化

社交网络环境中的营销整合了当前的先进互联网和信息技术。这种营销方式不仅拓宽了营销渠道，搭建了网络交流平台，还满足了整个市场客户的购买需求，缩短了客户购买的时间。这个过程也是时代发展的必然产物。但是，当公司使用社交网络进行产品营销时，它们通常会面临多种现实因素的影响。本文以影响力最大化为主要目标，为产品信息传播寻找种子用户，并考虑了用户的兴趣偏好。目标用户可以影响产品，公司应控制营销成本以获得更大的边际收益。基于此，本文考虑了信息传播规模，用户兴趣偏好和公司预算等因素，将影响力最大化模型作为多目标优化问题，并提出了影响力的多目标最大化（MOIM）模型。为了解决NP影响最大的NP难题，本文采用蒙特卡洛采样法来计算高影响力用户。接下来，提出了一种基于NSGA-II的种子用户选择算法，以优化上述三个目标函数并找到最优解。我们使用真实的社交网络数据来验证模型和方法的性能。实验表明，该模型可以生成合适的种子集，并可以满足信息传播的不同目的。敏感性分析证明我们的模型在不同的实际条件下是鲁棒的。