Scalable Inference of Customer Similarities from Interactions Data Using Dirichlet Processes
Scalable Inference of Customer Similarities from Interactions Data Using Dirichlet Processes
复制标题
使用狄利克雷过程从交互数据中可扩展地推断客户相似性
DOI：
10.1287/mksc.1110.0640
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
André Bonfrer
中科院分区：
文献类型：
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作者：
Michael Braun;André Bonfrer
根据社会学的同质性理论，彼此相似的人更有可能相互交往。营销人员通常可以获得客户之间互动的数据，以同质性为指导原则，可以对潜在的相似性进行推断。然而，更大的网络面临着需要建模的潜在交互数量的二次爆炸。这种可扩展性问题使得社交互动的概率模型在计算上对于除了最小网络之外的所有网络都是不可行的。在本文中，我们开发了一个概率框架来建模客户交互，这是在同质性理论的基础上，是足够灵活的，以考虑随机变化，谁与谁互动。特别是，我们提出了一种新的贝叶斯非参数方法，使用狄利克雷过程，缓和的可扩展性问题，营销研究人员遇到的网络数据工作时。我们发现，这个框架是一个强大的方式来吸引洞察客户的潜在相似性，我们讨论了营销人员如何将这些见解应用到细分和定位活动。
Under the sociological theory of homophily, people who are similar to one another are more likely to interact with one another. Marketers often have access to data on interactions among customers from which, with homophily as a guiding principle, inferences could be made about the underlying similarities. However, larger networks face a quadratic explosion in the number of potential interactions that need to be modeled. This scalability problem renders probability models of social interactions computationally infeasible for all but the smallest networks. In this paper, we develop a probabilistic framework for modeling customer interactions that is both grounded in the theory of homophily and is flexible enough to account for random variation in who interacts with whom. In particular, we present a novel Bayesian nonparametric approach, using Dirichlet processes, to moderate the scalability problems that marketing researchers encounter when working with networked data. We find that this framework is a powerful way to draw insights into latent similarities of customers, and we discuss how marketers can apply these insights to segmentation and targeting activities.