Utilization of Clustering Techniques and Markov Chains for Long-Tail Item Recommendation Systems

Diogo Silva, Diogo Silva, Davi Silva da Cruz, Diego Corrêa da Silva, João Dias de Almeida, Frederico Durão

2024

Abstract

The primary goal of this paper is to develop recommendation models that guide users to niche but highly relevant items in the long tail. Two major clustering techniques and representing matrices through graphs are explored for this. The first technique adopts Markov chains to calculate similarities of the nodes of a user-item graph. The second technique applies clustering to the set of items in a dataset. The results show that it is possible to improve the accuracy of the recommendations even by focusing on less popular items, in this case, niche products that form the long tail. The recall in some cases improved by about 27.9%, while the popularity of recommended items has declined. In addition, the recommendations to contain more diversified items indicate better exploitation of the long tail. Finally, an online experiment was conducted using an evaluation questionnaire with the employees of the HomeCenter store, providing the dataset. The aim is to analyze the performance of the proposed algorithms directly with the users. The results showed that the evaluators preferred the proposed algorithms, demonstrating the proposed approaches’ effectiveness.

Paper Citation

in Harvard Style

Silva D., Silva da Cruz D., Corrêa da Silva D., Dias de Almeida J. and Durão F. (2024). Utilization of Clustering Techniques and Markov Chains for Long-Tail Item Recommendation Systems. In Proceedings of the 20th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST; ISBN 978-989-758-718-4, SciTePress, pages 47-58. DOI: 10.5220/0012936700003825

in Bibtex Style

@conference{webist24,
author={Diogo Silva and Davi Silva da Cruz and Diego Corrêa da Silva and João Dias de Almeida and Frederico Durão},
title={Utilization of Clustering Techniques and Markov Chains for Long-Tail Item Recommendation Systems},
booktitle={Proceedings of the 20th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST},
year={2024},
pages={47-58},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012936700003825},
isbn={978-989-758-718-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST
TI - Utilization of Clustering Techniques and Markov Chains for Long-Tail Item Recommendation Systems
SN - 978-989-758-718-4
AU - Silva D.
AU - Silva da Cruz D.
AU - Corrêa da Silva D.
AU - Dias de Almeida J.
AU - Durão F.
PY - 2024
SP - 47
EP - 58
DO - 10.5220/0012936700003825
PB - SciTePress