Exploring Strategies to Mitigate Cold Start in Recommender Systems: A Systematic Literature Mapping

Nathalia Cezar, Isabela Gasparini, Daniel Lichtnow, Gabriel Lunardi, José Moreira de Oliveira

2024

Abstract

Recommender Systems are designed to provide personalized item recommendations to users based on their preferences and behavioral patterns, aiming to suggest items that align with their interests and profile. In Recommender Systems, a common issue arises when the user’s profile is not adequately characterized, particularly at the initial stages of using the system. This issue has persisted in Recommender Systems since its inception, commonly known as Cold Start. The Cold Start issue, which impacts new users, is called User Cold Start. Through a systematic literature mapping, this paper identifies strategies to minimize User Cold Start without reliance on external sources (such as social networks) or user demographic data for initializing the profile of new users. The systematic literature mapping results present strategies aimed at mitigating the User Cold Start Problem, serving as a foundational resource for further enhancements or novel proposals beyond those identified in the review. Thus, the goal of this work is to understand how to create an initial user profile before any prior interaction and without using external sources in the recommender system.

Paper Citation

in Harvard Style

Cezar N., Gasparini I., Lichtnow D., Lunardi G. and Moreira de Oliveira J. (2024). Exploring Strategies to Mitigate Cold Start in Recommender Systems: A Systematic Literature Mapping. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-692-7, SciTePress, pages 965-972. DOI: 10.5220/0012550700003690

in Bibtex Style

@conference{iceis24,
author={Nathalia Cezar and Isabela Gasparini and Daniel Lichtnow and Gabriel Lunardi and José Moreira de Oliveira},
title={Exploring Strategies to Mitigate Cold Start in Recommender Systems: A Systematic Literature Mapping},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2024},
pages={965-972},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012550700003690},
isbn={978-989-758-692-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Exploring Strategies to Mitigate Cold Start in Recommender Systems: A Systematic Literature Mapping
SN - 978-989-758-692-7
AU - Cezar N.
AU - Gasparini I.
AU - Lichtnow D.
AU - Lunardi G.
AU - Moreira de Oliveira J.
PY - 2024
SP - 965
EP - 972
DO - 10.5220/0012550700003690
PB - SciTePress