Due to underlying privacy-sensitive information in user-item interaction data, the risk of
privacy leakage exists in the centralized-training recommender system (RecSys). To this …

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 …

Ever since the Turing Test was proposed in the 1950s, humans have explored the mastering
of language intelligence by machine. Language is essentially a complex, intricate system of …

In the past decades, recommender systems have attracted much attention in both research
and industry communities. Existing recommendation models mainly learn the underlying …

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 …

Recently, large language models such as ChatGPT have showcased remarkable abilities in
solving general tasks, demonstrating the potential for applications in recommender systems …

Sequential recommendation is an important task to predict the next-item to access based on
a sequence of interacted items. Most existing works learn user preference as the transition …

Recently, Graph Neural Networks (GNNs) have become a mainstream recommender system
method, where it captures high-order collaborative signals between nodes by performing …

With the capacity to capture high-order collaborative signals, Graph Neural Networks
(GNNs) have emerged as powerful methods in Recommender Systems (RS). However, their …

Learning effective high-order feature interactions is very crucial in the CTR prediction task.
However, it is very time-consuming to calculate high-order feature interactions with massive …