Confidence-aware matrix factorization for recommender systems
Collaborative filtering (CF), particularly matrix factorization (MF) based methods, have been
widely used in recommender systems. The literature has reported that matrix factorization …
widely used in recommender systems. The literature has reported that matrix factorization …
Using explainability for constrained matrix factorization
B Abdollahi, O Nasraoui - Proceedings of the eleventh ACM conference …, 2017 - dl.acm.org
Accurate model-based Collaborative Filtering (CF) approaches, such as Matrix Factorization
(MF), tend to be black-box machine learning models that lack interpretability and do not …
(MF), tend to be black-box machine learning models that lack interpretability and do not …
Boosting response aware model-based collaborative filtering
Recommender systems are promising for providing personalized favorite services.
Collaborative filtering (CF) technologies, making prediction of users' preference based on …
Collaborative filtering (CF) technologies, making prediction of users' preference based on …
Leveraging tagging for neighborhood-aware probabilistic matrix factorization
Collaborative Filtering (CF) is a popular way to build recommender systems and has been
successfully employed in many applications. Generally, two kinds of approaches to CF, the …
successfully employed in many applications. Generally, two kinds of approaches to CF, the …
Leveraging clustering to improve collaborative filtering
N Mirbakhsh, CX Ling - Information Systems Frontiers, 2018 - Springer
Extensive work on matrix factorization (MF) techniques have been done recently as they
provide accurate rating prediction models in recommendation systems. Additional …
provide accurate rating prediction models in recommendation systems. Additional …
Response aware model-based collaborative filtering
Previous work on recommender systems mainly focus on fitting the ratings provided by
users. However, the response patterns, ie, some items are rated while others not, are …
users. However, the response patterns, ie, some items are rated while others not, are …
Matrix factorization with rating completion: An enhanced SVD model for collaborative filtering recommender systems
Collaborative filtering algorithms, such as matrix factorization techniques, are recently
gaining momentum due to their promising performance on recommender systems. However …
gaining momentum due to their promising performance on recommender systems. However …
Incremental collaborative filtering recommender based on regularized matrix factorization
X Luo, Y Xia, Q Zhu - Knowledge-Based Systems, 2012 - Elsevier
The Matrix-Factorization (MF) based models have become popular when building
Collaborative Filtering (CF) recommenders, due to the high accuracy and scalability …
Collaborative Filtering (CF) recommenders, due to the high accuracy and scalability …
SCMF: sparse covariance matrix factorization for collaborative filtering
Matrix factorization (MF) is a popular collaborative filtering approach for recommender
systems due to its simplicity and effectiveness. Existing MF methods either assume that all …
systems due to its simplicity and effectiveness. Existing MF methods either assume that all …
Matrix factorization for recommendation with explicit and implicit feedback
S Chen, Y Peng - Knowledge-Based Systems, 2018 - Elsevier
Matrix factorization (MF) methods have proven as efficient and scalable approaches for
collaborative filtering problems. Numerous existing MF methods rely heavily on explicit …
collaborative filtering problems. Numerous existing MF methods rely heavily on explicit …