Recommender systems have become crucial in information filtering nowadays. Existing
recommender systems extract user preferences based on the correlation in data, such as …

Causal inference has recently garnered significant interest among recommender system
(RS) researchers due to its ability to dissect cause-and-effect relationships and its broad …

Multimedia recommendation forms a personalized ranking task with multimedia content
representations which are mostly extracted via generic encoders. However, the generic …

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

Recommendation systems aim to predict users' feedback on items not exposed to them yet.
Confounding bias arises due to the presence of unmeasured variables (eg, the socio …

Recommender systems are important and powerful tools for various personalized services.
Traditionally, these systems use data mining and machine learning techniques to make …

Traditional recommenders suffer from hidden confounding factors, leading to the spurious
correlations between user/item profiles and user preference prediction, ie, the confounding …

In the era of information overload, recommender systems (RSs) have become an
indispensable part of online service platforms. Traditional RSs estimate user interests and …

Deep neural networks have demonstrated remarkable performance in point cloud
classification. However previous works show they are vulnerable to adversarial …

Causality is gaining more and more attention in the machine learning community and
consequently also in recommender systems research. The limitations of learning offline from …