While recent years have witnessed a rapid growth of research papers on recommender
system (RS), most of the papers focus on inventing machine learning models to better fit …

Graph mining algorithms have been playing a significant role in myriad fields over the years.
However, despite their promising performance on various graph analytical tasks, most of …

Recommender systems rely on user behavior data like ratings and clicks to build
personalization model. However, the collected data is observational rather than …

Collaborative filtering (CF) models easily suffer from popularity bias, which makes
recommendation deviate from users' actual preferences. However, most current debiasing …

The prevalence of graph structures in real-world scenarios enables important tasks such as
node classification and link prediction. Graphs in many domains follow a long-tailed …

Recently, embedding techniques have achieved impressive success in recommender
systems. However, the embedding techniques are data demanding and suffer from the cold …

The cold-start problem has been a long-standing issue in recommendation. Embedding-
based recommendation models provide recommendations by learning embeddings for each …

Recommender systems (RS) have demonstrated great success in information seeking.
Recent years have witnessed a large number of work on inventing recommendation models …

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

Recommender system usually suffers from severe popularity bias—the collected interaction
data usually exhibits quite imbalanced or even long-tailed distribution over items. Such …