Reading or viewing recommendations are a common feature on modern media sites. What
is shown to consumers as recommendations is nowadays often automatically determined by …

Recommender system usually faces popularity bias issues: from the data perspective, items
exhibit uneven (usually long-tail) distribution on the interaction frequency; from the method …

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 …

Recommender systems (RSs) aim at hel** users to effectively retrieve items of their
interests from a large catalogue. For a quite long time, researchers and practitioners have …

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

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

Social recommender systems are expected to improve recommendation quality by
incorporating social information when there is little user-item interaction data. However …

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

Abstract Knowledge Distillation (KD) aims at transferring knowledge from a larger well-
optimized teacher network to a smaller learnable student network. Existing KD methods …

There exist complex patterns behind the decision-making processes of different individuals
across different environments. For instance, in a social recommender system, various user …