In collaborative filtering recommendations, part of studies mainly focus on quantitatively
learning user preferences based on explicit rating information. However, due to the …

Recommender systems remain an essential topic due to its wide application in various
domains and the business potential behind them. With the rise of deep learning, common …

Ground Penetrating Radar (GPR) inversion typically relies on iterative methods, which often
involve high computational complexity and challenges in noise handling. These limitations …

To address the challenge of relying on complex biochemical methods for identifying rice
species, a prediction model that combines gas chromatography-ion mobility spectroscopy …

While traditional recommendation techniques have made significant strides in the past
decades, they still suffer from limited generalization performance caused by factors like …

Most recommendation systems are implemented using numerical or categorical data, that is,
traditional data. This type of data can be a limiting factor when used to model complex …