Session Similarity based Approach for Alleviating Cold-start Session Problem in e-Commerce for Top-N Recommendations

Ramazan Esmeli, Mohamed Bader-El-Den, Hassana Abdullahi

2020

Abstract

Cold-Start problem is one of the main challenges for the recommender systems. There are many methods developed for traditional recommender systems to alleviate the drawback of cold-start user and item problems. However, to the best of our knowledge, in session based recommender systems cold-start session problem still needs to be investigated. In this paper, we propose a session similarity-based method to alleviate drawback of cold-start sessions in e-commerce domain, in which there are no interacted items in the sessions that can help to identify users’ preferences. In the proposed method, product recommendations are given based on the most similar sessions that are found using session features such as session start time, location, etc. Computational experiments on two real-world datasets show that when the proposed method applied, there is a significant improvement on the performance of recommender systems in terms of recall and precision metrics comparing to random recommendations for cold-start sessions.

Paper Citation

in Harvard Style

Esmeli R., Bader-El-Den M. and Abdullahi H. (2020). Session Similarity based Approach for Alleviating Cold-start Session Problem in e-Commerce for Top-N Recommendations. In Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 1: KDIR; ISBN 978-989-758-474-9, SciTePress, pages 179-186. DOI: 10.5220/0010107001790186

in Bibtex Style

@conference{kdir20,
author={Ramazan Esmeli and Mohamed Bader-El-Den and Hassana Abdullahi},
title={Session Similarity based Approach for Alleviating Cold-start Session Problem in e-Commerce for Top-N Recommendations},
booktitle={Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 1: KDIR},
year={2020},
pages={179-186},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010107001790186},
isbn={978-989-758-474-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 1: KDIR
TI - Session Similarity based Approach for Alleviating Cold-start Session Problem in e-Commerce for Top-N Recommendations
SN - 978-989-758-474-9
AU - Esmeli R.
AU - Bader-El-Den M.
AU - Abdullahi H.
PY - 2020
SP - 179
EP - 186
DO - 10.5220/0010107001790186
PB - SciTePress