An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems
Matrix-factorization (MF)-based approaches prove to be highly accurate and scalable in
addressing collaborative filtering (CF) problems. During the MF process, the non-negativity …
addressing collaborative filtering (CF) problems. During the MF process, the non-negativity …
A nonnegative latent factor model for large-scale sparse matrices in recommender systems via alternating direction method
Nonnegative matrix factorization (NMF)-based models possess fine representativeness of a
target matrix, which is critically important in collaborative filtering (CF)-based recommender …
target matrix, which is critically important in collaborative filtering (CF)-based recommender …
[책][B] Natural language processing for social media
A Farzindar, D Inkpen, G Hirst - 2015 - Springer
In recent years, online social networking has revolutionized interpersonal communication.
The newer research on language analysis in social media has been increasingly focusing …
The newer research on language analysis in social media has been increasingly focusing …
Algorithms of unconstrained non-negative latent factor analysis for recommender systems
Non-negativity is vital for a latent factor (LF)-based model to preserve the important feature
of a high-dimensional and sparse (HiDS) matrix in recommender systems, ie, none of its …
of a high-dimensional and sparse (HiDS) matrix in recommender systems, ie, none of its …
Latent factor-based recommenders relying on extended stochastic gradient descent algorithms
High-dimensional and sparse (HiDS) matrices generated by recommender systems contain
rich knowledge regarding various desired patterns like users' potential preferences and …
rich knowledge regarding various desired patterns like users' potential preferences and …
An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data
High-dimensional and sparse (HiDS) data with non-negativity constraints are commonly
seen in industrial applications, such as recommender systems. They can be modeled into an …
seen in industrial applications, such as recommender systems. They can be modeled into an …
Tag completion for image retrieval
Many social image search engines are based on keyword/tag matching. This is because tag-
based image retrieval (TBIR) is not only efficient but also effective. The performance of TBIR …
based image retrieval (TBIR) is not only efficient but also effective. The performance of TBIR …
Non-negativity constrained missing data estimation for high-dimensional and sparse matrices from industrial applications
High-dimensional and sparse (HiDS) matrices are commonly seen in big-data-related
industrial applications like recommender systems. Latent factor (LF) models have proven to …
industrial applications like recommender systems. Latent factor (LF) models have proven to …
Tri-clustered tensor completion for social-aware image tag refinement
Social image tag refinement, which aims to improve tag quality by automatically completing
the missing tags and rectifying the noise-corrupted ones, is an essential component for …
the missing tags and rectifying the noise-corrupted ones, is an essential component for …
An efficient second-order approach to factorize sparse matrices in recommender systems
Recommender systems are an important kind of learning systems, which can be achieved
by latent-factor (LF)-based collaborative filtering (CF) with high efficiency and scalability. LF …
by latent-factor (LF)-based collaborative filtering (CF) with high efficiency and scalability. LF …