This paper presents a recommender system for tourism based on the tastes of the users, their demographic classification and the places they have visited in former trips. The system is able to offer recommendations for a single user or a group of users. The group recommendation is elicited out of the individual personal recommendations through the application of mechanisms such as aggregation an...

Recommender systems are data filtering systems that suggest data items of interest by predicting user preferences. In this paper, we describe the recommender system developed by the team named uefs.br for the offline competition of the 15th ECML PKDD Discovery Challenge 2013 on building a recommendation system for given names. The proposed system is a hybrid recommender system that applied a co...

Recommender systems are data filtering systems that suggest data items of interest by predicting user preferences. In this paper, we describe the recommender system developed by the team named uefs.br for the offline competition of the 15th ECML PKDD Discovery Challenge 2013 on building a recommendation system for given names. The proposed system is a hybrid recommender system that applied a co...

The paper identifies possibilities of using Web 2.0 tools and ELARS (E-learning Activities Recommender System) in e-learning. Changes that are present in e-learning due to influence of Web 2.0 incited an approach of combining an LMS with Web 2.0 tools and educational recommender system. Some preliminary results of the research project "E-learning Recommender System" are presented. One of the ma...

Recommender Systems (RSs) are garnering a significant importance with the advent of e-commerce and ebusiness on the web. This paper focused on the Movie Recommender System (MRS) based on human emotions. The problem is the MRS need to capture exactly the customer’s profile and features of movies, therefore movie is a complex domain and emotions is a human interaction domain, so difficult to comb...

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. This paper describes a new recommender system, which employs a particle swarm optimization (PSO) algorithm to learn personal preferences of users and provide tailored suggestions. Experiments are carried out to observe the pe...

In this paper, we introduce a novel situation-aware approach to improve a context based recommender system. To build situationaware user profiles, we rely on evidence issued from retrieval situations. A retrieval situation refers to the social-spatiotemporal context of the user when he interacts with the recommender system. A situation is represented as a combination of socialspatiotemporal con...

In recent years recommender systems (RSs) has gained popularity to solve the problem of web information overload and redundancy. Recommendation system helps users in finding the contents of their interest with minimum efforts. Even though most of the systems use explicit rating to recommend the content of users interest. When reading the electronic books performance of user gets affected becaus...

Article history: Received March 29, 2011 Received in Revised form June, 18, 2011 Accepted 19 June 2011 Available online 20 June 2011 Recommender systems are tools for realization one to one marketing. Recommender systems are systems, which attract, retain, and develop customers. Recommender systems use several ways to make recommendations. Two ways are using more than the others: collaborative ...

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...