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Authors: Ho Vy 1 ; 2 ; Tiet Hong 1 ; 2 ; Vu Hang 1 ; 2 ; Cuong Pham-Nguyen 1 ; 2 and Le Nguyen Hoai Nam 1 ; 2

Affiliations: 1 Faculty of Information Technology, University of Science, Ho Chi Minh City, Vietnam ; 2 VietNam National University, Ho Chi Minh City, Vietnam

Keyword(s): Preference Similarity, Collaborative Filtering, Recommender System.

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.

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Paper citation in several formats:
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 - DATA; ISBN 978-989-758-664-4; ISSN 2184-285X, SciTePress, pages 532-539. DOI: 10.5220/0012135500003541

@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 - DATA},
year={2023},
pages={532-539},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012135500003541},
isbn={978-989-758-664-4},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Data Science, Technology and Applications - DATA
TI - A Multi-Factor Approach to Measure User Preference Similarity in Neighbor-Based Recommender Systems
SN - 978-989-758-664-4
IS - 2184-285X
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

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