Collaborative filtering (CF) methods produce recommendations based on usage patterns
without the need of exogenous information about items or users. CF algorithms have shown …

Embedding based models have been the state of the art in collaborative filtering for over a
decade. Traditionally, the dot product or higher order equivalents have been used to …

Graph convolutional networks (GCNs) have recently enabled a popular class of algorithms
for collaborative filtering (CF). Nevertheless, the theoretical underpinnings of their empirical …

Numerical evaluations with comparisons to baselines play a central role when judging
research in recommender systems. In this paper, we show that running baselines properly is …

Implicit feedback is widely used in collaborative filtering methods for recommendation. It is
well known that implicit feedback contains a large number of values that are\emph {missing …

User preferences behind users' decision-making processes are highly diverse and may
range from lower-level concepts with more specific intentions and higher-level concepts with …

Recent studies on learning with noisy labels have shown remarkable performance by
exploiting a small clean dataset. In particular, model agnostic meta-learning-based label …

Dynamic interaction graphs have been widely adopted to model the evolution of user-item
interactions over time. There are two crucial factors when modelling user preferences for link …

Capturing the dynamics in user preference is crucial to better predict user future behaviors
because user preferences often drift over time. Many existing recommendation algorithms …

The Internet's rapid growth has led to information overload, necessitating recommender
systems for personalized suggestions. While content-based and collaborative filtering have …