The ubiquity of implicit feedback makes them the default choice to build online
recommender systems. While the large volume of implicit feedback alleviates the data …

With the rapid growth of information, recommender systems have become integral for
providing personalized suggestions and overcoming information overload. However, their …

Sequential recommendation models, models that learn from chronological user-item
interactions, outperform traditional recommendation models in many settings. Despite the …

Thanks to their flexibility and scalability, collaborative embedding-based models are widely
employed for the top-N recommendation task. Their goal is to jointly represent users and …

Context: Recommender systems are widely used in various fields because they can provide
decision-making guidance to users facing an overwhelming set of choices. In previous …

In domains where users tend to develop long-term preferences that do not change too
frequently, the stability of recommendations is an important factor of the perceived quality of …

Travel recommender systems, also called (TRS) have recently gained significant attention in
the research and industrial communities. These systems aim at identifying the travellers …

The ubiquity of implicit feedback makes it indispensable for building recommender systems.
However, it does not actually reflect the actual satisfaction of users. For example, in E …

A collaborative filtering-based recommendation system has been an integral part of e-
commerce and e-servicing. To keep the recommendation systems reliable, authentic, and …

Collaborative filtering (CF) is a novel statistical technique developed to retrieve useful
information and to generate predictions based on provided data from users. It is …