Recommender systems often grapple with noisy implicit feedback. Most studies alleviate the
noise issues from data cleaning perspective such as data resampling and reweighting, but …

With the emergence of social networks, social recommendation has become an essential
technique for personalized services. Recently, graph-based social recommendations have …

Modern recommender systems have seen substantial success, yet they remain vulnerable to
malicious activities, notably poisoning attacks. These attacks involve injecting malicious data …

As its availability and generality in online services, implicit feedback is more commonly used
in recommender systems. However, implicit feedback usually presents noisy samples in real …

Sequential recommendation systems fundamentally rely on users' historical interaction
sequences, which are often contaminated by noisy interactions. Identifying these noisy …

Sequential recommender systems (SRSs) aim to suggest next item for a user based on her
historical interaction sequences. Recently, many research efforts have been devoted to …

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

Training recommendation models on large datasets requires significant time and resources.
It is desired to construct concise yet informative datasets for efficient training. Recent …

Recommender systems (RSs) have become an essential tool for mitigating information
overload in a range of real-world applications. Recent trends in RSs have revealed a major …

Recently, recommender system has achieved significant success. However, due to the
openness of recommender systems, they remain vulnerable to malicious attacks …