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Title: Predicting prices of italian fashion products using text data. A survey of methods
Authors: Federico, Crescenzi 
Issue Date: 2022
This study proposes a comparison of hedonic pricing models using
attributes obtained by featurizing text. We collected prices of
items sold in the websites of five famous fashion producers to estimate
hedonic pricing models leveraging the information contained in products’
descriptions. After each description is mapped to a point into
a high-dimensional feature space, we compare sparse modelling, topic
modelling and aggregated predictors to find the model with the best
out-of-sample predictive performance. We refer to this approach as Hedonic
Text-Regression modelling. With this approach, we estimate the
implicit price of words used in descriptions. Empirically, the proposed
models outperform the traditional hedonic pricing model in terms of
predictive accuracy while providing also consistent variable selection.
ISSN: 1863-8171
Appears in Collections:A1. Articolo in rivista

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