Category Mining by Heterogeneous Data Fusion Using PdLSI Model in a Retail Service
Category Mining by Heterogeneous Data Fusion Using PdLSI Model in a Retail Service
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零售服务中使用 PdLSI 模型的异构数据融合进行类别挖掘
DOI：
10.1109/icdm.2010.83
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
Y. Motomura
中科院分区：
文献类型：
--
作者：
Tsukasa Ishigaki;T. Takenaka;Y. Motomura
本文介绍了一种适当的类别发现方法，同时涉及客户的生活方式类别和项目类别的可持续管理的零售服务，指定为“类别挖掘”。类别挖掘是使用大规模的ID-POS数据和客户的调查问卷的答复，他们的生活方式。针对异构数据融合问题，提出了一种概率双潜在语义索引（PdLSI）模型，它是PLSI模型的扩展。在PdLSI模型中，客户和项目的概率分为一些潜在的生活方式类别和潜在的项目类别。然后，利用贝叶斯网络建模实现了对潜在类别与各种购买情况之间关系的理解。该方法为有效的客户关系管理和分类管理提供了基于大规模数据的有用知识，并可应用于其他服务行业。
This paper describes an appropriate category discovery method that simultaneously involves a customer's lifestyle category and item category for the sustainable management of retail services, designated as ``category mining''. Category mining is realized using a large-scale ID-POS data and customer's questionnaire responses with respect to their lifestyle. For the heterogeneous data fusion, we propose a probabilistic double-latent semantic indexing (PdLSI) model that is an extension of PLSI model. In the PdLSI model, customers and items are classified probabilistically into some latent lifestyle categories and latent item category. Then, understanding of relation between the latent categories and various purchased situations is realized using Bayesian network modeling. This method provides useful knowledge based on a large-scale data for efficient customer relationship management and category management, and can be applicable for other service industries.