Feature fusion based deep neural collaborative filtering model for fertilizer prediction
B Swaminathan, S Palani… - Expert Systems with …, 2023 - Elsevier
With the advent of the modern era, deep neural networks have dominated recommender
systems, as they can effectively capture complex interactions. Nevertheless, there is still a …
systems, as they can effectively capture complex interactions. Nevertheless, there is still a …
[HTML][HTML] MOOCs video recommendation using low-rank and sparse matrix factorization with inter-entity relations and intra-entity affinity information
Y Gao - Information Processing & Management, 2024 - Elsevier
Purpose The serious information overload problem of MOOCs videos decreases the
learning efficiency of the students and the utilization rate of the videos. There are two …
learning efficiency of the students and the utilization rate of the videos. There are two …
Self-supervised learning for fair recommender systems
Data-driven recommender algorithms are widely used in many systems, such as e-
commerce recommender systems and movie recommendation systems. However, these …
commerce recommender systems and movie recommendation systems. However, these …
IUG-CF: Neural collaborative filtering with ideal user group labels
Demographics are crucial information for recommender systems (RSs). Most existing
demographic-based RSs focus on similarity between user profiles. However, they rarely …
demographic-based RSs focus on similarity between user profiles. However, they rarely …
Reduced-dimensional skip-inception feature-aggregated classified proportional-integral-derivative for suppression of mixed-mode oscillations in hydropower units
L Yin, B Fan - Electric Power Systems Research, 2023 - Elsevier
The existing methods cannot effectively distinguish and suppress mixed-mode oscillations in
hydro-dominated grid operation caused by different factors. This work proposes a reduced …
hydro-dominated grid operation caused by different factors. This work proposes a reduced …
Field-aware variational autoencoders for billion-scale user representation learning
User representation learning plays an essential role in Internet applications, such as
recommender systems. Though developing a universal embedding for users is demanding …
recommender systems. Though developing a universal embedding for users is demanding …
An improved constrained Bayesian probabilistic matrix factorization algorithm
G Wang, M Chen, J Wu, M Fan, Q Liu - Soft Computing, 2023 - Springer
Given the increasing growth of the Web and consequently the growth of e-commerce, the
application of recommendation systems becomes more and more extensive. A good …
application of recommendation systems becomes more and more extensive. A good …
Soft computing for recommender systems and sentiment analysis
The World Wide Web is becoming a bottomless source of unstructured data, with quintillions
of bytes of data generated daily and publicly accessible [1]. Social media, customer reviews …
of bytes of data generated daily and publicly accessible [1]. Social media, customer reviews …
A restaurant recommendation method that combines neural network algorithms and information extraction from electronic word of mouth
Recommendation systems are popular information systems that help consumers manage
the information overload problem, encountered when making decisions with many …
the information overload problem, encountered when making decisions with many …
Taxonomy and Implications of Machine Learning for Internet of Things: Qualities, Uses and Algorithms
Abstract The future Internet of Things will have profound economic, commercial and societal
implications. IoT nodes are often resource restricted, making them attractive targets for cyber …
implications. IoT nodes are often resource restricted, making them attractive targets for cyber …