Abstract
Recommendation systems, which are employed to mitigate the information overload e-commerce users face, have succeeded in aiding customers during their online shopping experience. However, to be able to make accurate recommendations, these systems require information about the items for sale and about users’ individual preferences. Making recommendations to new customers, who have no prior data in the system, is therefore challenging. This scenario, called the “cold-start problem,” hinders the accuracy of recommendations made to a new user. In this paper, we introduce the popular users personalized predictions (PUPP-DA) framework to address cold starts. Soft clustering and active learning are used to accurately recommend items to new users in this framework. Additionally, we employ deep learning algorithms to improve the overall predictive accuracy. Experimental evaluation shows that the PUPP-DA framework results in high performance and accurate predictions. Further, focusing on frequent, or so-called popular, users during our active-learning stage clearly benefits the learning process.
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Alabdulrahman, R., Viktor, H., Paquet, E. (2020). Active Learning and Deep Learning for the Cold-Start Problem in Recommendation System: A Comparative Study. In: Fred, A., Salgado, A., Aveiro, D., Dietz, J., Bernardino, J., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2019. Communications in Computer and Information Science, vol 1297. Springer, Cham. https://doi.org/10.1007/978-3-030-66196-0_2
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