A Multi-Factor Approach to Measure User Preference Similarity in Neighbor-Based Recommender Systems

Ho Vy, Ho Vy, Tiet Hong, Tiet Hong, Vu Hang, Vu Hang, Cuong Pham-Nguyen, Cuong Pham-Nguyen, Le Nguyen Hoai Nam, Le Nguyen Hoai Nam

2023

Abstract

Neighbor-based Collaborative filtering is one of the commonly applied techniques in recommender systems. It is highly appreciated for its interpretability and ease of implementation. The effectiveness of neighbor-based collaborative filtering depends on the selection of a user preference similarity measure to identify neighbor users. In this paper, we propose a user preference similarity measure named Multi-Factor Preference Similarity (MFPS). The distinctive feature of our proposed method is its efficient combination of the four key factors in determining user preference similarity: rating commodity, rating usefulness, rating details, and rating time. Our experiments have demonstrated that the combination of these factors in our proposed method has achieved good results on both experimental datasets: Movielens 100K and Personality-2018.

Paper Citation

in Harvard Style

Vy H., Hong T., Hang V., Pham-Nguyen C. and Nguyen Hoai Nam L. (2023). A Multi-Factor Approach to Measure User Preference Similarity in Neighbor-Based Recommender Systems. In Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-664-4, SciTePress, pages 532-539. DOI: 10.5220/0012135500003541

in Bibtex Style

@conference{data23,
author={Ho Vy and Tiet Hong and Vu Hang and Cuong Pham-Nguyen and Le Nguyen Hoai Nam},
title={A Multi-Factor Approach to Measure User Preference Similarity in Neighbor-Based Recommender Systems},
booktitle={Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2023},
pages={532-539},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012135500003541},
isbn={978-989-758-664-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - A Multi-Factor Approach to Measure User Preference Similarity in Neighbor-Based Recommender Systems
SN - 978-989-758-664-4
AU - Vy H.
AU - Hong T.
AU - Hang V.
AU - Pham-Nguyen C.
AU - Nguyen Hoai Nam L.
PY - 2023
SP - 532
EP - 539
DO - 10.5220/0012135500003541
PB - SciTePress