Recommender systems help users find relevant items of interest, for example on e-
commerce or media streaming sites. Most academic research is concerned with approaches …

Recommender systems are tools that support online users by pointing them to potential
items of interest in situations of information overload. In recent years, the class of session …

The benefits of neural approaches are undisputed in many application areas. However,
today's research practice in applied machine learning---where researchers often use a …

In recent years, research has been done on applying Recurrent Neural Networks (RNNs) as
recommender systems. Results have been promising, especially in the session-based …

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

Session-based recommendation focuses on the prediction of user actions based on
anonymous sessions and is a necessary method in the lack of user historical data. However …

Recent advances in sequence-aware approaches for session-based recommendation, such
as those based on recurrent neural networks, highlight the importance of leveraging …

Session-based recommendation is the task of recommending the next item a user might be
interested in given partially known session information, eg, part of a session or recent …

Session-based recommendation tries to make use of anonymous session data to deliver
high-quality recommendations under the condition that user profiles and the complete …

Given e-commerce scenarios that user profiles are invisible, session-based
recommendation is proposed to generate recommendation results from short sessions …