Explainable recommendation attempts to develop models that generate not only high-quality
recommendations but also intuitive explanations. The explanations may either be post-hoc …

Recommender system (RS) has emerged as a major research interest that aims to help
users to find items online by providing suggestions that closely match their interest. This …

In order to develop effective sequential recommenders, a series of sequence representation
learning (SRL) methods are proposed to model historical user behaviors. Most existing SRL …

Conversational recommender systems (CRS) aim to recommend high-quality items to users
through interactive conversations. Although several efforts have been made for CRS, two …

Modeling customer shopping intentions is a crucial task for e-commerce, as it directly
impacts user experience and engagement. Thus, accurately understanding customer …

Heterogeneous information network (HIN) has been widely adopted in recommender
systems due to its excellence in modeling complex context information. Although existing …

With the revival of neural networks, many studies try to adapt powerful sequential neural
models, ıe Recurrent Neural Networks (RNN), to sequential recommendation. RNN-based …

Recommender systems (RS), serving at the forefront of Human-centered AI, are widely
deployed in almost every corner of the web and facilitate the human decision-making …

In recent years online advertising has become increasingly ubiquitous and effective.
Advertisements shown to visitors fund sites and apps that publish digital content, manage …

The interactions of users and items in recommender system could be naturally modeled as a
user-item bipartite graph. In recent years, we have witnessed an emerging research effort in …