Over the past two decades, a large amount of research effort has been devoted to
develo** algorithms that generate recommendations. The resulting research progress has …

Context-aware recommender systems leverage the value of recommendations by exploiting
context information that affects user preferences and situations, with the goal of …

The debut of ChatGPT has recently attracted significant attention from the natural language
processing (NLP) community and beyond. Existing studies have demonstrated that ChatGPT …

Graph Convolution Network (GCN) has become new state-of-the-art for collaborative
filtering. Nevertheless, the reasons of its effectiveness for recommendation are not well …

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 …

Recommender systems typically retrieve items from an item corpus for personalized
recommendations. However, such a retrieval-based recommender paradigm faces two …

Click-through rate (CTR) prediction, which aims to predict the probability of a user clicking
on an ad or an item, is critical to many online applications such as online advertising and …

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 …

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