Recommender systems have become an essential tool to help resolve the information
overload problem in recent decades. Traditional recommender systems, however, suffer …

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

In the existing recommender systems, matrix factorization (MF) is widely applied to model
user preferences and item features by mapping the user-item ratings into a low-dimension …

In order to support the study of recent advances in recommender systems, this paper
presents an extended recommendation library consisting of eight packages for up-to-date …

Recommender systems have been studied extensively due to their practical use in real-
world scenarios. Despite this, generating effective recommendations with sparse user …

Existing benchmark datasets for recommender systems (RS) either are created at a small
scale or involve very limited forms of user feedback. RS models evaluated on such datasets …

In recommendation systems, the choice of loss function is critical since a good loss may
significantly improve the model performance. However, manually designing a good loss is a …

The Web has accumulated a rich source of information, such as text, image, rating, etc,
which represent different aspects of user preferences. However, the heterogeneous nature …

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

Many recent state-of-the-art recommender systems such as D-ATT, TransNet and
DeepCoNN exploit reviews for representation learning. This paper proposes a new neural …