In recent years, neural architecture-based recommender systems have achieved
tremendous success, but they still fall short of expectation when dealing with highly sparse …

Recommender system is one of the most important information services on today's Internet.
Recently, graph neural networks have become the new state-of-the-art approach to …

Contrastive learning (CL) recently has spurred a fruitful line of research in the field of
recommendation, since its ability to extract self-supervised signals from the raw data is well …

Recent advancements of sequential deep learning models such as Transformer and BERT
have significantly facilitated the sequential recommendation. However, according to our …

Deep learning on graphs has recently achieved remarkable success on a variety of tasks,
while such success relies heavily on the massive and carefully labeled data. However …

Contrastive learning (CL) has recently been demonstrated critical in improving
recommendation performance. The underlying principle of CL-based recommendation …

Session-based recommendation targets next-item prediction by exploiting user behaviors
within a short time period. Compared with other recommendation paradigms, session-based …

Most modern recommender systems predict users' preferences with two components: user
and item embedding learning, followed by the user-item interaction modeling. By utilizing …

In order to support the study of recent advances in recommender systems, this paper
presents an extended recommendation library consisting of eight packages for up-to-date …

With the prevalence of social media, there has recently been a proliferation of
recommenders that shift their focus from individual modeling to group recommendation …