Survey on multi-output learning
The aim of multi-output learning is to simultaneously predict multiple outputs given an input.
It is an important learning problem for decision-making since making decisions in the real …
It is an important learning problem for decision-making since making decisions in the real …
[HTML][HTML] Artificial intelligence in recommender systems
Recommender systems provide personalized service support to users by learning their
previous behaviors and predicting their current preferences for particular products. Artificial …
previous behaviors and predicting their current preferences for particular products. Artificial …
Position-transitional particle swarm optimization-incorporated latent factor analysis
High-dimensional and sparse (HiDS) matrices are frequently found in various industrial
applications. A latent factor analysis (LFA) model is commonly adopted to extract useful …
applications. A latent factor analysis (LFA) model is commonly adopted to extract useful …
A data-characteristic-aware latent factor model for web services QoS prediction
How to accurately predict unknown quality-of-service (QoS) data based on observed ones is
a hot yet thorny issue in Web service-related applications. Recently, a latent factor (LF) …
a hot yet thorny issue in Web service-related applications. Recently, a latent factor (LF) …
Fast and accurate non-negative latent factor analysis of high-dimensional and sparse matrices in recommender systems
A fast non-negative latent factor (FNLF) model for a high-dimensional and sparse (HiDS)
matrix adopts a Single Latent Factor-dependent, Non-negative, Multiplicative and …
matrix adopts a Single Latent Factor-dependent, Non-negative, Multiplicative and …
Recommender systems based on graph embedding techniques: A review
Y Deng - IEEE Access, 2022 - ieeexplore.ieee.org
As a pivotal tool to alleviate the information overload problem, recommender systems aim to
predict user's preferred items from millions of candidates by analyzing observed user-item …
predict user's preferred items from millions of candidates by analyzing observed user-item …
An L1-and-L2-Norm-Oriented Latent Factor Model for Recommender Systems
A recommender system (RS) is highly efficient in filtering people's desired information from
high-dimensional and sparse (HiDS) data. To date, a latent factor (LF)-based approach …
high-dimensional and sparse (HiDS) data. To date, a latent factor (LF)-based approach …
Temporal pattern-aware QoS prediction via biased non-negative latent factorization of tensors
Quality-of-service (QoS) data vary over time, making it vital to capture the temporal patterns
hidden in such dynamic data for predicting missing ones with high accuracy. However …
hidden in such dynamic data for predicting missing ones with high accuracy. However …
Optimizing weighted extreme learning machines for imbalanced classification and application to credit card fraud detection
The classification problems with imbalanced datasets widely exist in real word. An Extreme
Learning Machine is found unsuitable for imbalanced classification problems. This work …
Learning Machine is found unsuitable for imbalanced classification problems. This work …
Robust latent factor analysis for precise representation of high-dimensional and sparse data
High-dimensional and sparse (HiDS) matrices commonly arise in various industrial
applications, eg, recommender systems (RSs), social networks, and wireless sensor …
applications, eg, recommender systems (RSs), social networks, and wireless sensor …