The ubiquity of user-item interactions makes it essential and challenging to utilize the rich
variety of hidden structural and temporal information for effective and efficient …

User intention is an important factor to be considered for recommender systems, which
always changes dynamically in different contexts. Recent studies (represented by sequential …

Personalized recommendation plays a critical role in providing decision-making support for
product and service analysis in the field of business intelligence. Recently, deep neural …

Temporal recommendation which recommends items to users with consideration of time
information has been of wide interest in recent years. But huge event space, highly sparse …

Nowadays, recommendation systems are widely used on various services. This system
predicts what item a user will use next using large amounts of stored user history …

Pushing accurate and prompt recommendations to diverse users is an on-going key
challenge for researchers in studying recommender systems, which is also known as …

Modeling temporal behavior in recommendation systems is an important and challenging
problem. Its challenges come from the fact that temporal modeling increases the cost of …

Incorporating temporal information into recommender systems has recently attracted
increasing attention from both the industrial and academic research communities. Existing …

In recommendation, both stationary and dynamic user preferences on items are embedded
in the interactions between users and items (eg, rating or clicking) within their contexts …

The overload of information on the Internet becomes ubiquitous nowadays, which makes the
role of recommender systems more important. In recommender systems, the interest of users …