Three-Block Data Modeling by Endo- and Exo-LPLS Regression

Abstract

In consumer science it is common to study how various products are liked or ranked by various consumers. In this context, it is important to check if there are different consumer groups with different product preference patterns. If systematic consumer grouping is detected, it is important to determine the person characteristics which differentiate between these consumer segments, so that they can be reached selectively. Likewise it is important to determine the product characteristics that consumer segments seem to respond differently to.

Consumer preference data are usually rather noisy. The products ×persons data table (\vec{X}₁) usually produced in consumer preference studies may therefore be supplemented with two types of background information: a products ×product-property data table (\vec{X}₂) and a person ×person-property data table (\vec{X}₃). These additional data may be used for stabilizing the data modeling of the preference data \vec{X}₁ statistically. Moreover, they can reveal the product-properties that are responded to differently by the different consumer segments, and the person-properties that characterize these different segments. The present chapter outlines a recent approach to analyzing the three types of data tables in an integrated fashion and presents new modeling methods in this context.