A practical framework for real life webshop sales promotion targeting
In recent years online marketing has become increasingly extensive and effective. Product recommender systems are often deployed by e-commerce websites to improve user experience and increase sales. To address this, more and more e-commerce started to use machine learning models to predict customers purchase behaviors. In the scientific literature there are only few real-life studies to date which give solutions for recommendation systems for online advertising. The demand from the owners of such websites is given, however, it is hard for them to choose a method or model to predict from an endless number of options for some specific circumstances. The aim of this paper is to propose a practical guideline as a hybrid approach that predicts customers purchase behaviors and helps to target advertisement, sales form in user level. To this end, we have designed a robust hybrid model to predict interested sales form based on user behavior within a large e-commerce website. The aim of this paper is to detail a real-life practical solution and build a structure that can be used in a large variety of e-commerce systems.
Ahmed, A., Low, Y., Aly, M., Josifovski, V., andSmola, A. J. Scalable distributed inference of dynamic user interests for behavioral targeting, In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, 2011
Aly, M., Hatch, A., Josifovski, V., and Narayanan, V. K. Web-scale user modeling for targeting. Proceedings of the 21st International Conference on World Wide Web, 2012
Banerjee, A., and Ghosh, J. Clickstream Clustering using Weighted Longest Common Subse-
quences. In: The Web Mining Workshop at the 1st SIAM Conference on Data Mining, 2001
Bozanta, A., and Kutlu, B. Developing a Contextually Personalized Hybrid Recommender
System, Mobile Information Systems, Article ID 3258916, 2018
Breiman L. Random forests - random features. Technical Report 567, Statistics Depart-
ment, University of California, Berkeley, 1999
Burke, R. Hybrid recommender systems: Survey and experiments. User modeling and user-adapted interaction, 12(4), 331-370, 2002
Chen T. and Guestrin C. XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785-794, 2016
Cook R.D. Detection of influential observations in linear regression. Technometrics, 19(1):15- 18, 1977
John N. D. and Ratcliff D. Generalized iterative scaling for log-linear models. The Annals of Mathematical Statistics, 43(5):1470-1480, 1972
Çano, E., Morisio, M. Hybrid recommender systems: A systematic literature review. Intelligent Data Analysis, 21(6), 1487-1524, 2017
Essex, D. Matchmaker, matchmaker. Communications of the ACM, 52(5):16-17, 2009.
P. Geurts, D. Ernst., and L. Wehenkel, Extremely randomized trees. Machine Learning, 63(1), 3-42, 2006.
Grbovic, M., Radosavljevic, V., Djuric, N., Bhamidipati, N., Savla, J., Bhagwan, V., and Sharp, D.
E-commerce in Your Inbox: Product Recommendations at Scale. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015
Hoerl, A. E., Kennard, R. W., Ridge Regression: Applications to Non-Orthogonal Problems. Technometrics 12(1), 69-82, 1970
James G. Majority vote classifiers: theory and applications. PhD thesis, Stanford University, 1998
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T. Y. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In: Advances in neural information processing systems, pp. 3146-3154, 2017.
Park, T., Casella, G., The Bayesian Lasso. Journal of the American Statistical Association 103(482), 681-686, 2008
Sidana, S. Recommendation systems for online advertising. Computers and Society [cs.CY]. Université Grenoble Alpes, 2018.
Vieira, A. Predicting online user behaviour using deep learning algorithms. arXiv preprint arXiv:1511.06247, 2015
Weiss, A. A Comparison of Ordinary Least Squares and Least Absolute Error Estimation. Econometric Theory, 4(3), 517-527, 1988
Zhang, Y., and Pennacchiotti, M. Predicting purchase behaviors from social media, In: Proceedings of the 22nd international conference on World Wide Web, 2013
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