Hidden Variable Models for Market Basket Data. Statistical Performance and Managerial Implications

URN to cite this document:: urn:nbn:de:bvb:355-epub-349949
DOI to cite this document:: 10.5283/epub.34994

Hruschka, Harald

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Date of publication of this fulltext: 19 Dec 2016 12:28

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Item type:	Monograph (Working Paper)
Publisher:	Fac. of Business, Economics and Management Information Systems, Univ. of Regensburg
Place of Publication:	Regensburg
Series of the University of Regensburg:	Regensburger Diskussionsbeiträge zur Wirtschaftswissenschaft
Volume:	489
Number of Pages:	17
Date:	15 December 2016
Institutions:	Business, Economics and Information Systems > Institut für Betriebswirtschaftslehre > Lehrstuhl für Marketing (Prof. Dr. Harald Hruschka)
Keywords:	Marketing; Market Basket Analysis; Factor Analysis; Topic Models; Restricted Boltzmann Machine; Deep Belief Net
Dewey Decimal Classification:	300 Social sciences > 330 Economics
Status:	Unpublished
Refereed:	No, this document will not be refereed
Created at the University of Regensburg:	Yes
Item ID:	34994

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Abstract

We compare the performance of several hidden variable models, namely binary factor analysis, topic models (latent Dirichlet allocation, correlated topic model), the restricted Boltzmann machine and the deep belief net. We shortly present these models and outline their estimation. Performance is measured by log likelihood values of these models for a holdout data set of market baskets. For each ...

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