Classifying and recommending using gradient boosted machines and vector space models

Sheil, Humphrey and Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646 2017. Classifying and recommending using gradient boosted machines and vector space models. Presented at: 17th UK Workshop on Computational Intelligence, Cardiff, Wales, UK, 6-8 September 2017. Published in: Chao, F, Schockaert, Steven ORCID: https://orcid.org/0000-0002-9256-2881 and Zhang, Q eds. Advances in Computational Intelligence Systems. Advances in Intelligent Systems and Computing. Advances in Intelligent Systems and Computing, , vol.650 Cham: Springer, pp. 214-221. 10.1007/978-3-319-66939-7_18

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Abstract

Deciphering user intent from website clickstreams and providing more relevant product recommendations to users remains an important challenge in Ecommerce. We outline our approach to the twin tasks of user classification and content ranking in an Ecommerce setting using an open dataset. Design and development lessons learned through the use of gradient boosted machines are described and initial findings reviewed. We describe a novel application of word embeddings to the dataset chosen to model item-item similarity. A roadmap is proposed outlining future planned work.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Springer
ISBN:	978-3-319-66939-7
ISSN:	2194-5365
Last Modified:	03 Nov 2022 10:22
URI:	https://orca.cardiff.ac.uk/id/eprint/107814

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