Modeling customer shop** intentions is a crucial task for e-commerce, as it directly
impacts user experience and engagement. Thus, accurately understanding customer …

Pre-training models have shown their power in sequential recommendation. Recently,
prompt has been widely explored and verified for tuning after pre-training in NLP, which …

In this paper, we study the task of cold-start sequential recommendation, where new users
with very short interaction sequences come with time. We cast this problem as a few-shot …

Session-based recommendation aims to predict items that a user will interact with based on
historical behaviors in anonymous sessions. It has long faced two challenges:(1) the …

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

Session-based recommender systems (SBRSs) have shown superior performance over
conventional methods. However, they show limited scalability on large-scale industrial …

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

Abstract Few-Shot Learning (FSL) is a challenging task, ie, how to recognize novel classes
with few examples? Pre-training based methods effectively tackle the problem by pre …

Bundle recommendation, which seeks to recommend a group of items to users, is widely
used in real-world applications. Despite the success of current bundle recommendation …

Recently, many E-commerce search models are based on Graph Neural Networks (GNNs).
Despite their promising performances, they are (1) lacking proper semantic representation of …