Recommender system focuses on techniques that could predict user interest and give assistance while the user interacts with the Web in finding relevant information. It attempt to make sense of the data generated by his past interaction and predict in future choices. The focus of research in the area of recommender system has been on accuracy in the past decade, but the trend is changing with an...

with the rapid expansion of the information on the internet, recommender systems play an important role in terms of trade and research. recommender systems try to guess the user's way of thinking, using the in-formation of user's behavior or similar users and their views, to discover and then propose a product which is the most appropriate and closest product of user's interest. ...

With the rapid expansion of the information on the Internet, recommender systems play an important role in terms of trade and research. Recommender systems try to guess the user's way of thinking, using the in-formation of user's behavior or similar users and their views, to discover and then propose a product which is the most appropriate and closest product of user's interest. In the past dec...

The following article presents a Mash-up Personal Learning Environment called ReMashed that recommends items from the emerging information of a Learning Network. In ReMashed users can specify certain Web 2.0 services and combine them in a Mash-Up Personal Learning Environment. The users can rate information from an emerging amount of Web 2.0 information of a Learning Network and train a recomme...

The Internet and World Wide Web have brought us into a world of endless possibilities: interactive Web sites to experience, music to listen to, conversations to participate in, and every conceivable consumer item to order. But this world also is one of endless choice: how can we select from a huge universe of items of widely varying quality? Computational recommender systems have emerged to add...

The prosperity of e-commerce has changed the whole outlook of traditional trading behavior. More and more people are willing to conduct Internet shopping. However, the massive product information provided by the Internet Merchants causes the problem of information overload and this will reduces the customer’s satisfaction and interests. To overcome this problem, a recommender system based on we...

The popularity of group recommender systems has increased in the last years. More and more social activity is generated by users over the Web and thus not only domains as TV, music or holiday resorts are used and researched anymore for group recommendations, but also collaborative learning support, digital libraries and other domains seem to be promising for group recommendations. Moreover, pri...

Capturing and understanding users' context is key to the success of applications such as computational advertising and recommender systems. Currently, context is usually inferred from a user's interaction history with Web content. As an ever-increasing portion of Web interaction occurs in a mobile context, the physical and social environment in which the interaction occurs is a key factor of us...

We extend performance measures commonly used in semantic web applications to be capable of handling multi-graded relevance data. Most of today's recommender social web applications o er the possibility to rate objects with di erent levels of relevance. Nevertheless most performance measures in Information Retrieval and recommender systems are based on the assumption that retrieved objects (e. g...

The abundance of data published using Semantic Web technologies ratifies their high degree of maturity reached. Moreover, the flexibility of the Resource Description Framework (RDF) enables it to model any knowledge within a specific domain. This has given rise to a potential use of RDF data as input for applications which were not originally designed to operate online on Web data sources. Reco...