With the growing volume of online information, recommender systems have been an
effective strategy to overcome information overload. The utility of recommender systems …

These days, many recommender systems (RS) are utilized for solving information overload
problem in areas such as e-commerce, entertainment, and social media. Although classical …

Learning vector representations (aka. embeddings) of users and items lies at the core of
modern recommender systems. Ranging from early matrix factorization to recently emerged …

To provide more accurate, diverse, and explainable recommendation, it is compulsory to go
beyond modeling user-item interactions and take side information into account. Traditional …

In recent years, Graph Neural Networks (GNNs), which can naturally integrate node
information and topological structure, have been demonstrated to be powerful in learning on …

Personalized recommendation plays a central role in many online content sharing platforms.
To provide quality micro-video recommendation service, it is of crucial importance to …

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

Precise user and item embedding learning is the key to building a successful recommender
system. Traditionally, Collaborative Filtering (CF) provides a way to learn user and item …

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 …

Transformer is a powerful model for text understanding. However, it is inefficient due to its
quadratic complexity to input sequence length. Although there are many methods on …