TY - GEN
T1 - FeatureMF - A Novel Collaborative Filtering Recommendation Model
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) model, called FeatureMF, which takes into account item features and thus addresses the cold-start item and data sparsity problems of collaborative filtering (CF). More specifically, the model extends item latent vectors with item representation learned from metadata. Experiments conducted on a public dataset with two testing views confirm that FeatureMF achieves better recommendation performance than some of the popular state-of-the-art MF models.
AB - This paper1 presents a novel matrix factorization (MF) model, called FeatureMF, which takes into account item features and thus addresses the cold-start item and data sparsity problems of collaborative filtering (CF). More specifically, the model extends item latent vectors with item representation learned from metadata. Experiments conducted on a public dataset with two testing views confirm that FeatureMF achieves better recommendation performance than some of the popular state-of-the-art MF 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=85092715988&partnerID=8YFLogxK
U2 - 10.1109/MACISE49704.2020.00014
DO - 10.1109/MACISE49704.2020.00014
M3 - Conference Proceeding
AN - SCOPUS:85092715988
T3 - Proceedings - 2nd International Conference on Mathematics and Computers in Science and Engineering, MACISE 2020
SP - 34
EP - 37
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 -