Recommender systems (RSs) have been playing an increasingly important role for informed
consumption, services, and decision-making in the overloaded information era and digitized …

A well-informed recommendation framework could not only help users identify their
interested items, but also benefit the revenue of various online platforms (eg, e-commerce …

Sequential recommendation aims to capture users' dynamic preferences, in which data
sparsity is a key problem. Most contrastive learning models leverage data augmentation to …

Modeling time-evolving preferences of users with their sequential item interactions, has
attracted increasing attention in many online applications. Hence, sequential recommender …

Recommender systems have played a critical role in many web applications to meet user's
personalized interests and alleviate the information overload. In this survey, we review the …

Sequential recommendation aims at predicting the next item that the user may be interested
in given the historical interaction sequence. Typical neural models derive a single history …

Human behaviors in recommendation systems are driven by many high-level, complex, and
evolving intentions behind their decision making processes. In order to achieve better …

The problem of basket recommendation (BR) is to recommend a ranking list of items to the
current basket. Existing methods solve this problem by assuming the items within the same …

Research on recommendation systems is swiftly producing an abundance of novel methods,
constantly challenging the current state-of-the-art. Inspired by advancements in many …