Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …

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

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 …

Most recommender systems base their recommendations on implicit or explicit item-level
feedback provided by users. These item ratings are combined into a complex user model …

Despite the great success of many matrix factorization based collaborative filtering
approaches, there is still much space for improvement in recommender system field. One …

Both reviews and user-item interactions (ie, rating scores) have been widely adopted for
user rating prediction. However, these existing techniques mainly extract the latent …

In recent years, Multi-task Learning (MTL) has yielded immense success in Recommender
System (RS) applications [40]. However, current MTL-based recommendation models tend …

User reviews contain rich semantics towards the preference of users to features of items.
Recently, many deep learning based solutions have been proposed by exploiting reviews …

While neural recommenders have become the state-of-the-art in recent years, the complexity
of deep models still makes the generation of tangible explanations for end users a …

Graph neural networks (GNNs) have achieved remarkable performance in personalized
recommendation, for their powerful data representation capabilities. However, these …