Most modern recommender systems predict users' preferences with two components: user
and item embedding learning, followed by the user-item interaction modeling. By utilizing …

Graph neural networks (GNNs), with their promising potential to learn effective graph
representation, have been widely used for recommender systems, in which the given graph …

Electronic coaching (eCoach) facilitates goal-focused development for individuals to
optimize certain human behavior. However, the automatic generation of personalized …

Deep learning (DL)-based recommender system (RS), particularly for its advances in the
recent five years, has been startling. It reshapes the architectures of traditional RSs by lifting …

Tourism is an important industry and a popular entertainment activity involving billions of
visitors per annum. One challenging problem tourists face is identifying satisfactory products …

In digital marketing, the core advantages of scientific and technological means such as
artificial intelligence and big data analysis gradually appear and pay attention to them. This …

A High-dimensional and s parse (HiDS) matrix is frequently encountered in Big Data-related
applications such as e-commerce systems or wireless sensor networks. It is of great …

Existing review-based recommendation methods learn a latent representation of user and
item from user-generated reviews by a static strategy, which are unable to capture the …

Deep learning-based recommendations have demonstrated impressive performance in
improving recommendation accuracy. However, such approaches mainly utilize implicit …

Online users' feedback has numerous text comments to enrich the review quality on
mainstream platforms, such as Yelp and Google Maps. Reading through numerous review …