Currently, the sparsity of user–item interaction data limits the performance of recommender
systems. To alleviate the problems caused by data sparsity, researchers are devoted to …

Recommender systems have become an important instrument to connect people to
information. Sparse, complex, and rapidly growing data presents new challenges to …

As compared to the continuous temporal distributions, discrete data representations may be
desired for simplified and faster data analysis and forecasting. Data compression can …

Employing customer information from one of the world's largest airline companies, we
develop a price elasticity model (PREM) using machine learning to identify customers likely …

With the rapid development of social media and big data technology, user's sequence
behavior information can be well recorded and preserved on different media platforms. It is …

Since meta-paths have the innate ability to capture rich structure and semantic information,
meta-path-based recommendations have gained tremendous attention in recent years …

Recommender system as an effective method to reduce information overload has been
widely used in the e-commerce field. Existing studies mainly capture semantic features by …

Malaysia has experienced public health issues and economic losses due to air pollution
problems. As the air pollution problem keeps increasing over time, studies on air quality …

Multi-behavior recommendations effectively integrate various types of behaviors and have
been proven to enhance recommendation performance. However, existing researches …

Sentiment prediction is useful for scientific decision-making and reliable assessments in
various fields. One significant challenge of sentiment prediction is the difficulty in …