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User segmentation via interpretable user representation and relative similarity-based segmentation method

机译：通过可解释的用户表示和相对相似性的分段方法的用户分割

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摘要

User segmentation is an essential element of marketing and product development that considers customers' needs and recognizes the heterogeneity of those needs. In a key study of smartphone user segmentation, Lee et al. analyzed app usage sequencing using seq2seq architecture. However, despite achieving meaningful results, their approach could not provide a robust interpretation of user segmentation because the seq2seq architecture represented users in a continuous vector space generated from a black box model. In this paper, we propose an interpretable user representation method that combines app clustering with a novel segmentation method. The user representation clusters characteristically similar apps into common clusters, with each user represented by their frequencies of app use within their respective clusters. Two novel techniques are also applied to normalize the value of user representation based on the relative degrees of importance between app clusters and the membership strengths of individual apps within a cluster. Furthermore, to address the limitations of existing segmentation methods, in which the most closely located users are assigned to specific clusters, the proposed method segments represented users using a novel segmentation approach based on relative similarity. Experimental results demonstrate that the proposed method provides an intuitive interpretation for each user's representation and segmentation results. Furthermore, we effectively show the similarities between the results produced by our method and ground truth and demonstrate that it outperforms existing user segmentation methods.
机译：用户分割是营销和产品开发的必要因素，以考虑客户的需求，并认识到这些需求的异质性。在智能手机用户细分的关键研究中，Lee等人。使用SEQ2SEQ架构分析了应用程序使用量码。然而，尽管实现了有意义的结果，但它们的方法无法提供对用户分割的强大解释，因为SEQ2Seq架构代表了从黑盒模型生成的连续向量空间中的用户。在本文中，我们提出了一种可解释的用户表示方法，该方法将应用聚类与新颖的分段方法组合。用户表示将特性地与共同集群相似的应用程序，每个用户由它们在其各自的群集中使用的应用频率表示。还应用了两种新颖的技术来基于应用程序集群与集群内各个应用程序之间的相对程度的相对程度的相对程度来规范用户表示的值。此外，为了解决现有分割方法的限制，其中将最密切的用户分配给特定的集群，所提出的方法段代表了基于相对相似性的新的分段方法的用户。实验结果表明，该方法对每个用户的表示和分段结果提供了直观的解释。此外，我们有效地展示了我们的方法和地面真理产生的结果之间的相似性，并证明它优于现有的用户分段方法。

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