This paper reports on the design, implementation, and evaluation of a market-based recommender system that suggests relevant documents to users. The key feature of the system is t...
Group recommender systems introduce a whole set of new challenges for recommender systems research. The notion of generating a set of recommendations that will satisfy a group of ...
In a cluster of many servers containing heterogeneous multimedia learning material and serving users with different backgrounds (e.g. language, interests, previous knowledge, hardw...
Recommender systems are widely used to cope with the problem of information overload and, consequently, many recommendation methods have been developed. However, no one technique i...
We describe a recommender system which uses a unique combination of content-based and collaborative methods to suggest items of interest to users, and also to learn and exploit it...
Social tags are user-generated keywords associated with some resource on the Web. In the case of music, social tags have become an important component of “Web2.0” recommender ...
Douglas Eck, Paul Lamere, Thierry Bertin-Mahieux, ...
Recommender systems are an emerging technology that helps consumers to find interesting products. A recommender system makes personalized product suggestions by extracting knowle...
The amount of data exponentially increases in information systems and it becomes more and more difficult to extract the most relevant information within a very short time. Among ot...
One of the main problems faced by university students is deciding the right learning path based on available information such as courses, schedules and professors. In this context,...
The goal of the work in this paper is towards the incorporation of context in recommender systems in the domain of mobile applications. The approach recommends mobile applications...