Recommender systems use algorithms to provide users product recommendations. Recently, these systems started using machine learning algorithms because of the progress and popularity of the artificial intelligence research field. However, choosing the suitable machine learning algorithm is difficult because of the sheer number of algorithms available in the literature. Researchers and practition...

While recommender systems can greatly enhance the user experience, the entry barriers in terms of data acquisition are very high, making it hard for new service providers to compete with existing recommendation services. This paper proposes to build open recommender systems which can utilise Linked Data to mitigate the new-user, new-item and sparsity problems of collaborative recommender system...

Nowadays, recommender systems have been used to reduce information overload and to find the items that are of interest to the user. Many techniques have been proposed for providing recommendations to consumers or users. All currently available recommender techniques have strengths and weaknesses. Thus, numerous researcher studies have attempted to develop techniques that would overcome the vari...

Faced with overwhelming choice people seek advice from their peers or other trusted sources. Collaborative filter recommender systems aim to emulate this process by filtering all the options according to the user tastes expressed through prior item evaluations. Until now the recommender systems literature predominantly focused on improving the algorithms for making suitable predictions for unra...

Recommender system is a topic which falls under the domain of information retrieval, data mining and machine learning. Recommender systems are widely used by famous websites like Amazon, Flipcart, Netflix, Facebook, twitter and many others. There are various types of recommender systems like collaborative filtering, content based filtering and hybrid recommender system. Recommender systems can ...

Recommender systems, also known as recommender engines, have become an important research area and are now being applied in various fields. In addition, the techniques behind the recommender systems have been improved over the time. In general, such systems help users to find their required products or services (e.g. books, music) through analyzing and aggregating other users’ activities and be...

Recommender systems are becoming a salient part of many e-commerce websites. Much research has focused on advancing recommendation technologies to improve the accuracy of predictions, while behavioral aspects of using recommender systems are often overlooked. In this study, we explore how consumer preferences at the time of consumption are impacted by predictions generated by recommender system...

The great amount of educational resources available on educational repositories enriches the learning process. However, it raises a new challenge: the need to provide support to the location of those resources that meet the needs, goals and preferences of each student. The location of useful educational resources to support the learning process is addressed by using recommender systems. Recomme...

Recommender systems have proved really useful in order to handle with the information overload on the Internet. However, it is very difficult to evaluate such a personalised systems since this involves purely subjective assessments. Actually, only very few recommender systems developed over the Internet evaluate and discuss their results scientifically. The contribution of this paper is a metho...

Recommender systems are new types of internet-based software tools, designed to help users find their way through today’s complex on-line shops and entertainment websites. Here we provide an overview of current recommender systems, and then outline a new Lifestyle Recommender System, which employs techniques such as evolutionary search and a 3D avatar to provide tailored and friendly suggestion...