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.
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Sheil, H., Rana, O. (2018). Classifying and Recommending Using Gradient Boosted Machines and Vector Space Models. In: Chao, F., Schockaert, S., Zhang, Q. (eds) Advances in Computational Intelligence Systems. UKCI 2017. Advances in Intelligent Systems and Computing, vol 650. Springer, Cham. https://doi.org/10.1007/978-3-319-66939-7_18
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DOI: https://doi.org/10.1007/978-3-319-66939-7_18
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