Recommender system is one of the most important information services on today's Internet.
Recently, graph neural networks have become the new state-of-the-art approach to …

With the explosive growth of online information, recommender systems play a key role to
alleviate such information overload. Due to the important application value of recommender …

This chapter studies state-of-the-art research related to multimedia recommender systems
(MMRS), focusing on methods that integrate multimedia content as side information to …

The learning objective plays a fundamental role to build a recommender system. Most
methods routinely adopt either pointwise (eg, binary cross-entropy) or pairwise (eg, BPR) …

Leveraging the side information associated with entities (ie, users and items) to enhance
recommendation systems has been widely recognized as an essential modeling dimension …

The music domain is among the most important ones for adopting recommender systems
technology. In contrast to most other recommendation domains, which predominantly rely on …

Personalized news recommendation is an important task for online news platforms to target
user interests and alleviate information overload. Most existing methods leverage news …

Personalized recipe recommender systems help users mine certain dishes they want to find
and even really desire, which play a significant role in matching dishes, balancing nutrients …

Collaborative Filtering (CF) plays a crucial role in modern recommender systems, leveraging
historical user-item interactions to provide personalized suggestions. However, CF-based …

Advanced deep learning methods have been widely adopted in stock movement prediction
with technical analysis (TA), while researchers prefer technical indicators to technical charts …