Classifying and Recommending Using Gradient Boosted Machines and Vector Space Models

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 650)

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.

Keywords

Gradient boosted machine Classification Ranking Recommender system Vector space model Ecommerce 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.School of Computer Science and InformaticsCardiff UniversityCardiffUK

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