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

Sequential recommendation (SR) learns from the temporal dynamics of user-item
interactions to predict the next ones. Fairness-aware recommendation mitigates a variety of …

Sequential recommendation (SR) aims to predict the subsequent behaviors of users by
understanding their successive historical behaviors. Recently, some methods for SR are …

Conversational recommender systems (CRS) explicitly solicit users' preferences for
improved recommendations on the fly. Most existing CRS solutions count on a single policy …

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 …

Sequential recommendation models a user's historical sequence to predict future items.
Existing studies utilize deep learning methods and contrastive learning for data …

Session-based recommendation aims to predict intents of anonymous users based on their
limited behaviors. Modeling user behaviors involves two distinct rationales: co-occurrence …

Sequential recommender systems (SRSs) aim to suggest next item for a user based on her
historical interaction sequences. Recently, many research efforts have been devoted to …

Many modern online services feature personalized recommendations. A central challenge
when providing such recommendations is that the reason why an individual user accesses …