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2019 | OriginalPaper | Chapter

Combining Multiple Features for Product Categorisation by Multiple Kernel Learning

Authors : Chanawee Chavaltada, Kitsuchart Pasupa, David R. Hardoon

Published in: Recent Advances in Information and Communication Technology 2018

Publisher: Springer International Publishing

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Abstract

E-commerce provides convenience and flexibility for consumers; for example, they can inquire about the availability of a desired product and get immediate response, hence they can seamlessly search for any desired products. Every day, e-commerce sites are updated with thousands of new images and their associated metadata (textual information), causing a problem of big data. Retail product categorisation involves cross-modal retrieval that shows the path of a category. In this study, we leveraged both image vectors of various aspects and textual metadata as features, then constructed a set of kernels. Multiple Kernel Learning (MKL) proposes to combine these kernels in order to achieve the best prediction accuracy. We compared the Support Vector Machine (SVM) prediction results between using an individual feature kernel and an MKL combined feature kernel to demonstrate the prediction improvement gained by MKL.

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Metadata
Title
Combining Multiple Features for Product Categorisation by Multiple Kernel Learning
Authors
Chanawee Chavaltada
Kitsuchart Pasupa
David R. Hardoon
Copyright Year
2019
Book
Recent Advances in Information and Communication Technology 2018

Print ISBN: 978-3-319-93691-8

Electronic ISBN: 978-3-319-93692-5

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-319-93692-5_1

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