Modern recommender systems derive predictions from an interaction graph that links users
and items. To this end, many of today's state-of-the-art systems use graph neural networks …

Previous debiasing studies utilize unbiased data to make supervision of model training.
They suffer from the high trial risks and experimental costs to obtain unbiased data. Recent …

In recent years, recommender systems have achieved remarkable performance by using
ensembles of heterogeneous models. However, this approach is costly due to the resources …

Recommender systems (RSs) aim to provide suggestions for items that are most pertinent to
a particular user. Typically, RSs are trained and evaluated directly on the observed items …

Recommender models aimed at mining users' behavioral patterns have raised great
attention as one of the essential applications in daily life. Recent work on graph neural …

Debiased recommendation with a randomized dataset has shown very promising results in
mitigating system-induced biases. However, it still lacks more theoretical insights or an ideal …

Although debiasing in multimedia recommendation has shown promising results, most
existing work relies on the ability of the model itself to fully disentangle the biased and …

In recent years, dual-target Cross-Domain Recommendation (CDR) has been proposed to
capture comprehensive user preferences in order to ultimately enhance the …

Recent studies have highlighted the bias problem in recommender systems which affects
the learning of users' true preferences. One significant reason for bias is that the training …

Bundle recommendation approaches offer users a set of related items on a particular topic.
The current state-of-the-art (SOTA) method utilizes contrastive learning to learn …