In recent years, neural architecture-based recommender systems have achieved
tremendous success, but they still fall short of expectation when dealing with highly sparse …

The emergence of Pre-trained Language Models (PLMs) has achieved tremendous success
in the field of Natural Language Processing (NLP) by learning universal representations on …

Abstract Large Language Models (LLMs) have emerged as powerful tools in the field of
Natural Language Processing (NLP) and have recently gained significant attention in the …

Industrial recommender systems have been growing increasingly complex, may
involve\emph {diverse domains} such as e-commerce products and user-generated …

Multi-behavior recommendation (MBR) aims to jointly consider multiple behaviors to
improve the target behavior's performance. We argue that MBR models should:(1) model the …

Cross-domain recommendation (CDR) aims to provide better recommendation results in the
target domain with the help of the source domain, which is widely used and explored in real …

BERT4Rec is an effective model for sequential recommendation based on the Transformer
architecture. In the original publication, BERT4Rec claimed superiority over other available …

Recommender systems commonly suffer from the long-standing data sparsity problem
where insufficient user-item interaction data limits the systems' ability to make accurate …

Conversational recommender systems (CRS) aim to provide highquality recommendations
in conversations. However, most conventional CRS models mainly focus on the dialogue …

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 …