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 …

In the field of sequential recommendation, deep learning--(DL) based methods have
received a lot of attention in the past few years and surpassed traditional models such as …

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

Group recommendation aims to recommend items to a group of users. In this work, we study
group recommendation in a particular scenario, namely occasional group recommendation …

The goal of intent-aware session recommendation (ISR) approaches is to capture user
intents within a session for accurate next-item prediction. However, the capability of these …

Session-based recommendation (SBR), which mainly relies on a user's limited interactions
with items to generate recommendations, is a widely investigated task. Existing methods …

Sequential recommenders capture dynamic aspects of users' interests by modeling
sequential behavior. Previous studies on sequential recommendations mostly aim to identify …

Diversifying return results is an important research topic in retrieval systems in order to
satisfy both the various interests of customers and the equal market exposure of providers …

Cross-domain sequential recommendation is the task of predict the next item that the user is
most likely to interact with based on past sequential behavior from multiple domains. One of …

In contrast to traditional recommender systems which usually pay attention to users' general
and long-term preferences, sequential recommendation (SR) can model users' dynamic …