Recommender systems play a significant role in information filtering and have been utilized
in different scenarios, such as e-commerce and social media. With the prosperity of deep …

Deep learning-based recommender systems (DLRSs) often have embedding layers, which
are utilized to lessen the dimension of categorical variables (eg, user/item identifiers) and …

In practical recommendation scenarios, users often interact with items under multi-typed
behaviors (eg, click, add-to-cart, and purchase). Traditional collaborative filtering techniques …

As one of the most successful AI-powered applications, recommender systems aim to help
people make appropriate decisions in an effective and efficient way, by providing …

Sequential recommender systems aim to predict users' next interested item given their
historical interactions. However, a long-standing issue is how to distinguish between users' …

Multi-task Recommender Systems (MTRSs) has become increasingly prevalent in a variety
of real-world applications due to their exceptional training efficiency and recommendation …

Feature selection plays an impactful role in deep recommender systems, which selects a
subset of the most predictive features, so as to boost the recommendation performance and …

As the demand for more personalized recommendation grows and a dramatic boom in
commercial scenarios arises, the study on multi-scenario recommendation (MSR) has …