TY - JOUR
KW - Collaborative Filtering
KW - Matrix Factorization
KW - Recommendation Systems
AU - Jesús Bobadilla
AU - Jorge Dueñas-Lerín
AU - Fernando Ortega
AU - Abraham Gutiérrez
AB - Matrix factorization models are the core of current commercial collaborative filtering Recommender Systems. This paper tested six representative matrix factorization models, using four collaborative filtering datasets. Experiments have tested a variety of accuracy and beyond accuracy quality measures, including prediction, recommendation of ordered and unordered lists, novelty, and diversity. Results show each convenient matrix factorization model attending to their simplicity, the required prediction quality, the necessary recommendation quality, the desired recommendation novelty and diversity, the need to explain recommendations, the adequacy of assigning semantic interpretations to hidden factors, the advisability of recommending to groups of users, and the need to obtain reliability values. To ensure the reproducibility of the experiments, an open framework has been used, and the implementation code is provided.
IS - In Press
M1 - In Press
N2 - Matrix factorization models are the core of current commercial collaborative filtering Recommender Systems. This paper tested six representative matrix factorization models, using four collaborative filtering datasets. Experiments have tested a variety of accuracy and beyond accuracy quality measures, including prediction, recommendation of ordered and unordered lists, novelty, and diversity. Results show each convenient matrix factorization model attending to their simplicity, the required prediction quality, the necessary recommendation quality, the desired recommendation novelty and diversity, the need to explain recommendations, the adequacy of assigning semantic interpretations to hidden factors, the advisability of recommending to groups of users, and the need to obtain reliability values. To ensure the reproducibility of the experiments, an open framework has been used, and the implementation code is provided.
PY - 9998
SP - 1
EP - 9
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - Comprehensive Evaluation of Matrix Factorization Models for Collaborative Filtering Recommender Systems
UR - https://www.ijimai.org/journal/sites/default/files/2023-04/ip2023_04_008.pdf
VL - In Press
SN - 1989-1660
ER -