M
Martijn Kagie
Researcher at Erasmus University Rotterdam
Publications - 21
Citations - 153
Martijn Kagie is an academic researcher from Erasmus University Rotterdam. The author has contributed to research in topics: Recommender system & Product (mathematics). The author has an hindex of 8, co-authored 21 publications receiving 142 citations. Previous affiliations of Martijn Kagie include Erasmus Research Institute of Management.
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Journal ArticleDOI
A graphical shopping interface based on product attributes
TL;DR: In this paper, the authors propose to represent the mutual similarities of the recommended products in a two dimensional map, where similar products are located close to each other and dissimilar products far apart.
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A graphical shopping interface bases on product attributes
TL;DR: This work proposes to represent the mutual similarities of the recommended products in a two dimensional map, where similar products are located close to each other and dissimilar products far apart, and uses an adaptation of Gower's similarity coefficient based on the attributes of a product as a dissimilarity measure.
Journal IssueDOI
Hedonic price models and indices based on boosting applied to the Dutch housing market
Martijn Kagie,Michiel van Wezel +1 more
TL;DR: In this article, a hedonic price model for six geographical submarkets in the Netherlands is presented, based on a recent data-mining technique called boosting, which enables capturing complex nonlinear relationships and interaction effects between input variables.
Book ChapterDOI
Online shopping using a two dimensional product map
TL;DR: A user interface for online shopping that uses a two dimensional product map to present products and an application of this user interface to MP3 players is shown and an interpretation of the product map is given.
Book ChapterDOI
Map Based Visualization of Product Catalogs
TL;DR: In this article, a two dimensional map-based visualization of the recommendations is proposed to retain part of the information lost in content-and knowledge-based recommender systems, where two products with the same similarity to a query can differ from this query on a completely different set of product characteristics.