FeatureMF – A Novel Collaborative Filtering Recommendation Model
Haiyang Zhang, Иван Ганчев, Nikola S. Nikolov · 2020
This paper1presents 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.