A deep latent factor model for high-dimensional and sparse matrices in recommender systems
Recommender systems (RSs) commonly adopt a user-item rating matrix to describe users'
preferences on items. With users and items exploding, such a matrix is usually high …
preferences on items. With users and items exploding, such a matrix is usually high …
An efficient group recommendation model with multiattention-based neural networks
Z Huang, X Xu, H Zhu, MC Zhou - IEEE Transactions on Neural …, 2020 - ieeexplore.ieee.org
Group recommendation research has recently received much attention in a recommender
system community. Currently, several deep-learning-based methods are used in group …
system community. Currently, several deep-learning-based methods are used in group …
Advancing non-negative latent factorization of tensors with diversified regularization schemes
Dynamic relationships are frequently encountered in big data and services computing-
related applications, like dynamic data of user-side QoS in Web services. They are modeled …
related applications, like dynamic data of user-side QoS in Web services. They are modeled …
Evaluating collaborative filtering recommender algorithms: a survey
Due to the explosion of available information on the Internet, the need for effective means of
accessing and processing them has become vital for everyone. Recommender systems …
accessing and processing them has become vital for everyone. Recommender systems …
A novel deep learning-based collaborative filtering model for recommendation system
The collaborative filtering (CF) based models are capable of grasping the interaction or
correlation of users and items under consideration. However, existing CF-based methods …
correlation of users and items under consideration. However, existing CF-based methods …
A graph regularized non-negative matrix factorization method for identifying microRNA-disease associations
Motivation MicroRNAs (miRNAs) play crucial roles in post-transcriptional regulations and
various cellular processes. The identification of disease-related miRNAs provides great …
various cellular processes. The identification of disease-related miRNAs provides great …
Incorporation of efficient second-order solvers into latent factor models for accurate prediction of missing QoS data
Generating highly accurate predictions for missing quality-of-service (QoS) data is an
important issue. Latent factor (LF)-based QoS-predictors have proven to be effective in …
important issue. Latent factor (LF)-based QoS-predictors have proven to be effective in …
Centralized charging strategy and scheduling algorithm for electric vehicles under a battery swapping scenario
Centralized charging of electric vehicles (EVs) based on battery swapping is a promising
strategy for their large-scale utilization in power systems. The most outstanding feature of …
strategy for their large-scale utilization in power systems. The most outstanding feature of …
A deep reinforcement learning based long-term recommender system
L Huang, M Fu, F Li, H Qu, Y Liu, W Chen - Knowledge-Based Systems, 2021 - Elsevier
Recommender systems aim to maximize the overall accuracy for long-term
recommendations. However, most of the existing recommendation models adopt a static …
recommendations. However, most of the existing recommendation models adopt a static …
BAS-ADAM: An ADAM based approach to improve the performance of beetle antennae search optimizer
In this paper, we propose enhancements to Beetle Antennae search (BAS) algorithm, called
BAS-ADAM, to smoothen the convergence behavior and avoid trapping in local-minima for a …
BAS-ADAM, to smoothen the convergence behavior and avoid trapping in local-minima for a …