Recommender systems have been widely deployed in various real-world applications to
help users identify content of interest from massive amounts of information. Traditional …

To alleviate the problem of information explosion, recommender systems are widely
deployed to provide personalized information filtering services. Usually, embedding tables …

Recently, the generality of natural language text has been leveraged to develop transferable
recommender systems. The basic idea is to employ pre-trained language models (PLM) to …

Modern deep neural networks (DNNs) have greatly facilitated the development of sequential
recommender systems by achieving state-of-the-art recommendation performance on …

Approximate similarity queries on high-dimensional vector indices have become the
cornerstone for many critical online services. An increasing need for more sophisticated …

As one of the most successful AI-powered applications, recommender systems aim to help
people make appropriate decisions in an effective and efficient way, by providing …

Entity interaction prediction is essential in many important applications such as chemistry,
biology, material science, and medical science. The problem becomes quite challenging …

LLMs have marked a revolutonary shift, yet they falter when faced with compositional
reasoning tasks. Our research embarks on a quest to uncover the root causes of …

Leveraging the side information associated with entities (ie, users and items) to enhance
recommendation systems has been widely recognized as an essential modeling dimension …

Recommender systems have become an essential component of many online platforms,
providing personalized recommendations to users. A crucial aspect is embedding …