A recommender system is employed to accurately recommend items, which are expected to
attract the user's attention. The over-emphasis on the accuracy of the recommendations can …

Recommender systems are software mechanisms whose usage is to offer suggestions for
different types of entities like products, services, or contacts that could be useful or …

Most of the available recommender systems focus on the accuracy of recommendations. As
a result, their recommendations are often popular and very close to user preferences, which …

Next Point-of-Interest (POI) recommendation plays an important role in location-based
services. The state-of-the-art methods utilize recurrent neural networks (RNNs) to model …

Recommendation systems provide good guidance for users to find their favorite movies from
an overwhelming amount of options. However, most systems excessively pursue the …

Serendipity recommendation has attracted more and more attention in recent years; it is
committed to providing recommendations which could not only cater to users' demands but …

This paper introduces a frequent pattern mining framework for recommender systems
(FPRS)-a novel approach to address the items' cold-start problem. This difficulty occurs …

Serendipity has been recognized as a valuable property of recommender systems. While
there is a lack of consensus on the precise definition of serendipity, it is often conceptualized …

Conventional recommender systems have a potential problem of hyper-personalization that
produces biased recommendations, decreasing the diversity of items. Concerning this issue …

Although several approaches have been proposed throughout the last decade to build
recommender systems (RS), most of them suffer from the cold-start problem. This problem …