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 …
Improving matrix factorization-based recommender via ensemble methods
X Luo, Y Ouyang, X Zhang - International Journal of Information …, 2011 - World Scientific
One of the most popular approaches to Collaborative Filtering is based on Matrix
Factorization (MF). In this paper, we focus on improving MF-based recommender's accuracy …
Factorization (MF). In this paper, we focus on improving MF-based recommender's accuracy …
Matrix factorization meets cosine similarity: addressing sparsity problem in collaborative filtering recommender system
H Wen, G Ding, C Liu, J Wang - … and Applications: 16th Asia-Pacific Web …, 2014 - Springer
Matrix factorization (MF) technique has been widely used in collaborative filtering
recommendation systems. However, MF still suffers from data sparsity problem. Although …
recommendation systems. However, MF still suffers from data sparsity problem. Although …
Learning bidirectional asymmetric similarity for collaborative filtering via matrix factorization
Memory-based collaborative filtering (CF) aims at predicting the rating of a certain item for a
particular user based on the previous ratings from similar users and/or similar items …
particular user based on the previous ratings from similar users and/or similar items …
Deep probabilistic matrix factorization framework for online collaborative filtering
As living data growing and evolving rapidly, traditional machine learning algorithms are hard
to update models when dealing with new training data. When new data arrives, traditional …
to update models when dealing with new training data. When new data arrives, traditional …
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 …
Kernelized probabilistic matrix factorization for collaborative filtering: exploiting projected user and item graph
B Pal, M Jenamani - Proceedings of the 12th ACM conference on …, 2018 - dl.acm.org
Matrix Factorization (MF) techniques have already shown its strong foundation in
collaborative filtering (CF), particularly for rating prediction problem. In the basic MF model …
collaborative filtering (CF), particularly for rating prediction problem. In the basic MF model …
Integrating user-side information into matrix factorization to address data sparsity of collaborative filtering
Recommendation techniques play a vital role in recommending an actual product to an
intended user. The recommendation also supports the user in the decision-making process …
intended user. The recommendation also supports the user in the decision-making process …
[PDF][PDF] Sparse probabilistic matrix factorization by laplace distribution for collaborative filtering
L Jing, P Wang, L Yang - Twenty-Fourth International Joint Conference on …, 2015 - ijcai.org
In recommendation systems, probabilistic matrix factorization (PMF) is a state-of-the-art
collaborative filtering method by determining the latent features to represent users and …
collaborative filtering method by determining the latent features to represent users and …
Kernelized matrix factorization for collaborative filtering
Matrix factorization (MF) methods have shown great promise in collaborative filtering (CF).
Conventional MF methods usually assume that the correlated data is distributed on a linear …
Conventional MF methods usually assume that the correlated data is distributed on a linear …