Recommender systems aim to provide item recommendations for users and are usually
faced with data sparsity problems (eg, cold start) in real-world scenarios. Recently pre …

The advancement in internet technology has enabled the use of increasingly sophisticated
data by recommendation systems to enhance their effectiveness. This data is comprised of …

In recent years, there are numerous works been proposed to leverage the techniques of
deep learning to improve social-aware recommendation performance. In most cases, it …

Existing sequential recommendation methods rely on large amounts of training data and
usually suffer from the data sparsity problem. To tackle this, the pre-training mechanism has …

With the rapid development of the internet, more and more sophisticated data can be utilized
by recommendation systems to improve their performance. Such data consist of …

As an effective way to help users find attractive locations and meet their individual needs,
point-of-interest (POI) recommendation has become an important application in location …

With the rapid advancement of Internet, e-commerce websites and social networks, people
prefer to receive recommendations from their social friends rather than strangers. Also, the …

Heterogeneous Information Networks have bunches of rich secret information that assist us
in the creation of successful recommendation frameworks. A Heterogeneous Information …

To deal with the inherent data sparsity and cold-start problem, many recommender systems
try to exploit the textual information for improving prediction accuracy. Due to the significant …

Recent years witness the success of pre-trained models to alleviate the data sparsity
problem in recommender systems. However, existing pre-trained models for …