TY - GEN
T1 - Matrix Factorization Enriched with Item Features
AU - Zhang, Haiyang
AU - Ganchev, Ivan
AU - Nikolov, Nikola S.
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/1
Y1 - 2020/1
N2 - This paper1 presents a novel matrix factorization (MF) recommendation model, FeatureMF, which extends item latent vectors with item representation learned from metadata. By taking into account item features, the model addresses the cold-start item problem and data-sparsity problem of collaborative filtering (CF). Extensive experiments conducted on a public dataset with two testing views confirm that FeatureMF achieves better prediction accuracy than some of the popular state-of-the-art MF-based recommendation models.
AB - This paper1 presents a novel matrix factorization (MF) recommendation model, FeatureMF, which extends item latent vectors with item representation learned from metadata. By taking into account item features, the model addresses the cold-start item problem and data-sparsity problem of collaborative filtering (CF). Extensive experiments conducted on a public dataset with two testing views confirm that FeatureMF achieves better prediction accuracy than some of the popular state-of-the-art MF-based recommendation models.
KW - cold start
KW - collaborative filtering (CF)
KW - data sparsity
KW - matrix factorization (MF)
KW - recommendation model
UR - http://www.scopus.com/inward/record.url?scp=85092745328&partnerID=8YFLogxK
U2 - 10.1109/MACISE49704.2020.00020
DO - 10.1109/MACISE49704.2020.00020
M3 - Conference contribution
AN - SCOPUS:85092745328
T3 - Proceedings - 2nd International Conference on Mathematics and Computers in Science and Engineering, MACISE 2020
SP - 77
EP - 80
BT - Proceedings - 2nd International Conference on Mathematics and Computers in Science and Engineering, MACISE 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Mathematics and Computers in Science and Engineering, MACISE 2020
Y2 - 18 January 2020 through 20 January 2020
ER -