Session-based recommendation (SBR) is one of the hot research areas in recent years.
Various SBR models have been proposed, of which graph neural network (GNN)-based …

Recommender systems are essential to various fields, eg, e-commerce, e-learning, and
streaming media. At present, graph neural networks (GNNs) for session-based …

Recommender systems (RSs) have been extensively studied in academia and industry,
while few works focus on flash sale recommendations. In flash sale scenarios, period …

The goal of session-based recommendation (SBR) is to predict the next item at a certain
point in time for anonymous users. Previous methods usually learn session representations …

In many online recommendation services (eg, multimedia streaming, e-commerce),
predicting user's next behavior based on anonymous sessions remains a challenging …

Multi-interest collaborative metric learning has recently emerged as an effective approach to
modeling the multifaceted interests of a user in recommender systems. However, two issues …

Deep learning models have been successfully applied in sequential recommendations.
However, previous studies ignored the interaction between static and dynamic features of …

Session-based recommendation (SBR) faces the challenge of modeling user behavior
patterns within limited session sequences to predict the next item in anonymous sessions …

Abstract Knowledge Graph Embedding (KGE) based deep neural networks contribute to
recommender systems in diverse application scenarios. However, Catastrophic Forgetting …

Session-based recommendation, which aims at predicting subsequent user actions based
on anonymous sessions, plays a significant role in many online services. Based on the …