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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ben-Shimon, D., Tsikinovsky, A., Friedmann, M., Shapira, B., Rokach, L., Hoerle, J.: RecSys challenge 2015 and the YOOCHOOSE dataset. In: Proceedings of the 9th ACM Conference on Recommender Systems (RecSys 2015), pp. 357–358. ACM, New York (2015). https://doi.org/10.1145/2792838.2798723
Personal communication from David Ben-Shimon
Volkovs, M.: Two-stage approach to item recommendation from user sessions. In: Ben-Shimon, D., Friedmann, M., Rokach, L., Shapira, B. (eds.) Proceedings of the 2015 International ACM Recommender Systems Challenge (RecSys 2015 Challenge). ACM, New York (2015). Article 3, 4 p. http://dx.doi.org/10.1145/2813448.2813512
Romov, P., Sokolov, E.: RecSys challenge 2015: ensemble learning with categorical features. In: Ben-Shimon, D., Friedmann, M., Rokach, L., Shapira, B. (eds.) Proceedings of the 2015 International ACM Recommender Systems Challenge (RecSys 2015 Challenge). ACM, New York (2015). Article 1, 4 p. http://dx.doi.org/10.1145/2813448.2813510
Chen, T., Guestrin, C.: XGBoost: A Scalable Tree Boosting System (2016). http://arxiv.org/abs/1603.02754
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). http://dx.doi.org/10.1162/neco.1997.9.8.1735
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001). JSTOR. www.jstor.org/stable/2699986
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient Estimation of Word Representations in Vector Space (2013). https://arxiv.org/abs/1301.3781
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003). http://dx.doi.org/10.1109/MIC.2003.1167344
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-66939-7_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-66938-0
Online ISBN: 978-3-319-66939-7
eBook Packages: EngineeringEngineering (R0)