FeatureMF: an item feature enriched matrix factorization model for item recommendation

H Zhang, I Ganchev, NS Nikolov, Z Ji… - IEEE Access, 2021 - ieeexplore.ieee.org
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 …
被引用次数：31 相关文章

[引用][C] FeatureMF: An Item Feature Enriched Matrix Factorization Model for Item Recommendation

H Zhang, I Ganchev, NS Nikolov, Z Ji… - IEEE Access, 2021 - ui.adsabs.harvard.edu
FeatureMF: An Item Feature Enriched Matrix Factorization Model for Item Recommendation -
NASA/ADS Now on home page ads icon ads Enable full ADS view NASA/ADS FeatureMF: An …

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

H Zhang, I Ganchev, NS Nikolov, Z Ji, M O'Droma - IEEE Access, 2021 - scholar.xjtlu.edu.cn
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 …

FeatureMF: an item feature enriched matrix factorization model for item recommendation

H Zhang, I GANCHEV, NS Nikolov, Z Ji, M Ó'Droma - 2021 - researchrepository.ul.ie
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 …
[PDF] researchgate.net

[PDF][PDF] FeatureMF: An Item Feature Enriched Matrix Factorization Model for Item Recommendation

H ZHANG, I GANCHEV, NS NIKOLOV, Z JI… - researchgate.net
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 …
[PDF] ul.ie

[PDF][PDF] FeatureMF: An Item Feature Enriched Matrix Factorization Model for Item Recommendation

H ZHANG, I GANCHEV, NS NIKOLOV, Z JI… - researchrepository.ul.ie
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 …