As one of the most pervasive applications of machine learning, recommender systems are
playing an important role on assisting human decision-making. The satisfaction of users and …

Textual backdoor attacks pose significant security threats. Current detection approaches,
typically relying on intermediate feature representation or reconstructing potential triggers …

Recommender systems (RS) are effective tools for mitigating information overload and have
seen extensive applications across various domains. However, the single focus on utility …

Explainability is increasingly recognized as an enabling technology for the broader adoption
of machine learning (ML), particularly for safety-critical applications. This has given rise to …

Recommender systems may be confounded by various types of confounding factors (also
called confounders) that may lead to inaccurate recommendations and sacrificed …

Many of the traditional recommendation algorithms are designed based on the fundamental
idea of mining or learning correlative patterns from data to estimate the user-item correlative …

Model extraction (ME) attacks represent one major threat to Machine-Learning-as-a-Service
(MLaaS) platforms by" stealing" the functionality of confidential machine-learning models …

The rapid and accurate identification of bot accounts in online social networks is an ongoing
challenge. In this paper, we propose BotTriNet, a unified embedding framework that …

Previous studies on sequential recommendation (SR) have predominantly concentrated on
optimizing recommendation accuracy. However, there remains a significant gap in …

With the rapid development of online social networks recently, more and more online users
have participated in social network activities and rich social relationships are formed …