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 …

Traditional machine learning paradigms are based on the assumption that both training and
test data follow the same statistical pattern, which is mathematically referred to as …

In recommender systems, the collected data used for training is always subject to selection
bias, which poses a great challenge for unbiased learning. Previous studies proposed …

As information filtering services, recommender systems have extremely enriched our daily
life by providing personalized suggestions and facilitating people in decision-making, which …

Leading sequential recommendation (SeqRec) models adopt empirical risk minimization
(ERM) as the learning framework, which inherently assumes that the training data (historical …

Collaborative Filtering (CF) models, despite their great success, suffer from severe
performance drops due to popularity distribution shifts, where these changes are ubiquitous …

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

Knowledge graph (KG) demonstrates substantial potential for enhancing the performance of
recommender systems. Due to its rich semantic content and associations among interactive …

In leading collaborative filtering (CF) models, representations of users and items are prone
to learn popularity bias in the training data as shortcuts. The popularity shortcut tricks are …

Multimedia-based recommendation provides personalized item suggestions by learning the
content preferences of users. With the proliferation of digital devices and APPs, a huge …