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

The emerging topic of sequential recommender systems has attracted increasing attention in
recent years. Different from the conventional recommender systems including collaborative …

Click-Through Rate (CTR) prediction plays an important role in many industrial applications,
such as online advertising and recommender systems. How to capture users' dynamic and …

Learning user representations based on historical behaviors lies at the core of modern
recommender systems. Recent advances in sequential recommenders have convincingly …

We apply recurrent neural networks (RNN) on a new domain, namely recommender
systems. Real-life recommender systems often face the problem of having to base …

In session-based or sequential recommendation, it is important to consider a number of
factors like long-term user engagement, multiple types of user-item interactions such as …

The success of recommender systems often depends on their ability to understand and
make use of the context of the recommendation request. Significant research has focused on …

Real-life recommender systems often face the daunting task of providing recommendations
based only on the clicks of a user session. Methods that rely on user profiles--such as matrix …

Intelligence is the ability to learn from experience and use domain experts' knowledge to
adapt to new situations. In this context, an intelligent Recommender System should be able …

Recommender Systems are the set of tools and techniques to provide useful
recommendations and suggestions to the users to help them in the decision-making process …