Now showing items 1-6 of 6
Recommender Systems: Between Acceptance and Refusal
Recommender Systems (RSs) are a prominent solution to the problem of information overload on the web. It is impossible for users to process or even understand all information presented to them. Also, it becomes more and more difficult for an individual to identify appropriate concrete pieces of information or information ...
Influencing Factors for User Context in Proactive Mobile Recommenders
Proactive recommender systems break the standard request-response pattern of traditional recommenders by pushing item suggestions to the user when the situation seems appropriate. To support proactive recommendations in a mobile scenario, we have developed a two-phase proactivity model based on the current context of the ...
Mining Twitter for Cultural Patterns
Adaptive applications rely on the knowledge of their users, their needs and differences. For instance, in the scope of the ImReal 1 project, a training process is adapted to users’ origins using information on user cultural backgrounds. For inferring culture-specific information from available microblogging content, we ...
Adaptive User Interfaces on Tablets to Support People With Disabilities
With the advent of tablet computers, touch screens, gesture-based interaction and speech recognition, sophisticated applications with Natural User Interfaces (NUIs) become state of the art. NUIs have the potential to support people with disabilities, e.g., in their daily activities or in acquiring specific skills. Yet, ...
MERCURY: User Centric Device and Service Processing – Demo paper
In this paper, we present MERCURY, a platform for simple, user-centric integration and management of heterogeneous devices and services via a web-based interface. In contrast to existing approaches, MERCURY is geared towards non-IT-savvy end users. It enables these end users to easily interconnect devices, which can act ...
Integrating semantic relatedness in a collaborative filtering system
Collaborative Filtering (CF) recommender systems use opinions of people for filtering relevant information. The accuracy of these applications depends on the mechanism used to filter and combine the opinions (the feedback) provided by users. In this paper we propose a mechanism aimed at using semantic relations extracted ...