FeatureMF: An Item Feature Enriched Matrix Factorization Model for Item Recommendation

FeatureMF: An Item Feature Enriched Matrix Factorization Model for Item Recommendation

Matrix Factorization (MF) is one of the most successful Collaborative Filtering (CF) techniques used in recommender systems due to its effectiveness and ability to deal with very large user-item rating matrix. However, when the rating matrix sparseness increases its performance deteriorates. Expandi...

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Bibliographic Details
Main Authors: Haiyang Zhang, Ivan Ganchev, Nikola S. Nikolov, Zhanlin Ji, Mairtin O'Droma
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Collaborative filtering
matrix factorization
item features
cold start
data sparsity
Online Access:https://ieeexplore.ieee.org/document/9409095/

Internet

https://ieeexplore.ieee.org/document/9409095/

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