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 …
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 …
[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 …
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 …
Contextual collaborative filtering via hierarchical matrix factorization
Matrix factorization (MF) has been demonstrated to be one of the most competitive
techniques for collaborative filtering. However, state-of-the-art MFs do not consider …
techniques for collaborative filtering. However, state-of-the-art MFs do not consider …
[PDF][PDF] MPMA: Mixture Probabilistic Matrix Approximation for Collaborative Filtering.
Matrix approximation (MA) is one of the most popular techniques for collaborative filtering
(CF). Most existing MA methods train user/item latent factors based on a user-item rating …
(CF). Most existing MA methods train user/item latent factors based on a user-item rating …
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 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 …
Pair-wise preference relation based probabilistic matrix factorization for collaborative filtering in recommender system
A Pujahari, DS Sisodia - Knowledge-Based Systems, 2020 - Elsevier
Matrix Factorization (MF) is one of the most popular techniques used in Collaborative
Filtering (CF) based Recommender System (RS). Most of the MF methods tend to remove …
Filtering (CF) based Recommender System (RS). Most of the MF methods tend to remove …
Bayesian deep collaborative matrix factorization
In this paper, we propose a Bayesian Deep Collaborative Matrix Factorization (BDCMF)
algorithm for collaborative filtering (CF). BDCMF is a novel Bayesian deep generative model …
algorithm for collaborative filtering (CF). BDCMF is a novel Bayesian deep generative model …