Recommender systems are designed to help users in situations of information overload. In
recent years we observed increased interest in session-based recommendation scenarios …

In recent years, with the development of Internet technology, recommender systems have
been widely used by virtue of their ability to meet the personalized needs of users. In order …

Recommender systems help users find items they are likely to interact within the near future,
such as products to buy in e-commerce or songs to play in music websites. The Traditional …

Session-based recommender systems model the interests of users based on their browsing
behavior with the goal of making suitable item suggestions in an ongoing usage session …

Context-aware recommender systems (CARS) are specially designed to take into account
the contextual conditions under which a user experiences an item, with the goal of …

Traditional recommender systems help users find items of interest by modeling long-term
user profiles, which consist of items the users interacted with in the past. In many real-life …

The goal of session-based recommendation is to predict the next action of a user based on
the current session and anonymous sessions before. Recent works on session-based …

The personalized experience gets more and more attention these days. Many e-commerce
businesses are looking for methods to deliver personalized service. Consumers are …

In response to the evolving demands and challenges in recommendation systems, a
comprehensive and innovative approach is proposed for delivering personalized and …

The recommendation system can alleviate the problem of “information overload”, tap the
potential value of data, push personalized information to users in need, and improve …