A comprehensive review of recommender systems: Transitioning from theory to practice
Recommender Systems (RS) play an integral role in enhancing user experiences by
providing personalized item suggestions. This survey reviews the progress in RS inclusively …
providing personalized item suggestions. This survey reviews the progress in RS inclusively …
A comprehensive survey on deep learning techniques in educational data mining
Educational Data Mining (EDM) has emerged as a vital field of research, which harnesses
the power of computational techniques to analyze educational data. With the increasing …
the power of computational techniques to analyze educational data. With the increasing …
Adaptive multi-source data fusion vessel trajectory prediction model for intelligent maritime traffic
Multi-source vessel automatic identification system (AIS) data gathered in the maritime
Internet of Things (IoT) system has diverse data characteristics, such as sparse satellite data …
Internet of Things (IoT) system has diverse data characteristics, such as sparse satellite data …
Incorporating logic rules with textual representations for interpretable knowledge graph reasoning
Abstract Reasoning on knowledge graphs (KGs) is significant for downstream applications,
such as question answering and information extraction. On the basis of using factual triples …
such as question answering and information extraction. On the basis of using factual triples …
Knowledge-reinforced explainable next basket recommendation
L Huang, H Zou, XD Huang, Y Gao, Y Kuang… - Neural Networks, 2024 - Elsevier
The next basket recommendation task aims to predict the items in the user's next basket by
modeling the user's basket sequence. Existing next basket recommendations focus on …
modeling the user's basket sequence. Existing next basket recommendations focus on …
Interaction-knowledge semantic alignment for recommendation
In order to alleviate the issue of data sparsity, knowledge graphs are introduced into
recommender systems because they contain diverse information about items. The existing …
recommender systems because they contain diverse information about items. The existing …
Time series forecasting based on improved multi-linear trend fuzzy information granules for convolutional neural networks
R Zhang, J Zhan, W Ding… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Although the construction of multilinear trend fuzzy information granules (FIG) achieves a
win-win situation in terms of interpretability and trend extraction, in its second stage of …
win-win situation in terms of interpretability and trend extraction, in its second stage of …
Short‐term load forecasting based on a generalized regression neural network optimized by an improved sparrow search algorithm using the empirical wavelet …
GF Fan, Y Li, XY Zhang, YH Yeh… - Energy Science & …, 2023 - Wiley Online Library
With the development of the electric market, electric load forecasting has been increasingly
pursued by many scholars. Because the electric load is affected by many factors, it is …
pursued by many scholars. Because the electric load is affected by many factors, it is …
Cascaded Knowledge-Level Fusion Network for Online Course Recommendation System
W Ma, Y Zhao, X Fan - IEEE Transactions on Big Data, 2023 - ieeexplore.ieee.org
In light of the global proliferation of the COVID-19 pandemic, there is a notable surge in
public interest towards Massive Open Online Courses (MOOCs) recently. Within the realm of …
public interest towards Massive Open Online Courses (MOOCs) recently. Within the realm of …
A survey on temporal knowledge graph embedding: Models and applications
Abstract Knowledge graph embedding (KGE), as a pivotal technology in artificial
intelligence, plays a significant role in enhancing the logical reasoning and management …
intelligence, plays a significant role in enhancing the logical reasoning and management …