Active Learning and User Segmentation for the Cold-start Problem in Recommendation Systems

Rabaa Alabdulrahman, Herna Viktor, Eric Paquet

2019

Abstract

Recommendation systems, which are employed to mitigate the information overload faced by e-commerce users, 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 information about users’ individual preferences. Making recommendations to new customers, with 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) framework to address cold-starts. In this framework, soft clustering and active learning is used to accurately recommend items to new users. Experimental evaluation shows that the PUPP 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.

Paper Citation

in Harvard Style

Alabdulrahman R., Viktor H. and Paquet E. (2019). Active Learning and User Segmentation for the Cold-start Problem in Recommendation Systems. In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR; ISBN 978-989-758-382-7, SciTePress, pages 113-123. DOI: 10.5220/0008162901130123

in Bibtex Style

@conference{kdir19,
author={Rabaa Alabdulrahman and Herna Viktor and Eric Paquet},
title={Active Learning and User Segmentation for the Cold-start Problem in Recommendation Systems},
booktitle={Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR},
year={2019},
pages={113-123},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008162901130123},
isbn={978-989-758-382-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR
TI - Active Learning and User Segmentation for the Cold-start Problem in Recommendation Systems
SN - 978-989-758-382-7
AU - Alabdulrahman R.
AU - Viktor H.
AU - Paquet E.
PY - 2019
SP - 113
EP - 123
DO - 10.5220/0008162901130123
PB - SciTePress