Recommender systems (RSs) have been playing an increasingly important role for informed
consumption, services, and decision-making in the overloaded information era and digitized …

Recommender systems are one of the most successful applications of data mining and
machine-learning technology in practice. Academic research in the field is historically often …

User preferences are usually dynamic in real-world recommender systems, and a user» s
historical behavior records may not be equally important when predicting his/her future …

Deep learning methods have led to substantial progress in various application fields of AI,
and in recent years a number of proposals were made to improve recommender systems …

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 …

Recommendation systems are gaining increasing popularity in many application areas like
e-commerce, movie and music recommendations, tourism, news, advertisement, stock …

A user can be represented as what he/she does along the history. A common way to deal
with the user modeling problem is to manually extract all kinds of aggregated features over …

Context-aware recommender systems leverage the value of recommendations by exploiting
context information that affects user preferences and situations, with the goal of …

The importance of contextual information has been recognized by researchers and
practitioners in many disciplines, including e-commerce personalization, information …

Recommender systems play an important role in providing an engaging experience on
online music streaming services. However, the musical domain presents distinctive …