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

Representation learning on user-item graph for recommendation has evolved from using
single ID or interaction history to exploiting higher-order neighbors. This leads to the …

Knowledge graph (KG) plays an increasingly important role in recommender systems.
Recently, graph neural networks (GNNs) based model has gradually become the theme of …

The task of item recommendation requires ranking a large catalogue of items given a
context. Item recommendation algorithms are evaluated using ranking metrics that depend …

Incorporating knowledge graph into recommender systems has attracted increasing
attention in recent years. By exploring the interlinks within a knowledge graph, the …

Many predictive tasks of web applications need to model categorical variables, such as user
IDs and demographics like genders and occupations. To apply standard machine learning …

In recent years, deep neural networks have yielded immense success on speech
recognition, computer vision and natural language processing. However, the exploration of …

Graph collaborative filtering (GCF) is a popular technique for capturing high-order
collaborative signals in recommendation systems. However, GCF's bipartite adjacency …

Factorization Machines (FMs) are a supervised learning approach that enhances the linear
regression model by incorporating the second-order feature interactions. Despite …

Contrastive Learning (CL) has achieved impressive performance in self-supervised learning
tasks, showing superior generalization ability. Inspired by the success, adopting CL into …