RECOM Recommendation trends and roadmap
This project aims at defining the next functional steps towards an information relevance platform to be used by a Deutsche Telekom spinoff, Yoochoose. It is aimed at comparing recommendation technologies through benchmarks. It is also aimed at defining a set of metadata as they represent a critical factor for providing recommendations. In a second phase, we will design and test a system that can deliver music recommendations for a group of user and that will adapt the recommendations based on the contextual situation of the group.
Analytical Services for Medical Data Warehouse – MOBAS
In this project we focus on the exploitation of mobile and ubiquitous computing techniques in the hospital and eHealth scenarios. In this research project we aim at designing and implementing novel mobile services, which are integrated in the hospital information system, that support patients and clinicians in the day hospital scenario and their follow up at home. We want to enhance the effectiveness of the communication flow between the patients and the physicians by exploring the usage of several channels, including mobile devices, personalized web sites and large screens.
Real-Time Recommendation Revision and Explanation for a Network of Mobile Users – ReRex
The aim of this project is to advance the state of the art in recommender systems developing an effective methodology for supporting users in context-dependent real-time revision of personalized and contextualized recommendations. We have developed a methodology for acquiring in-contex ratings for items that has been adopted to acquire the data used in the mobile recommender (ReRex). ReRex mobile is an iPhone application providing context-aware recommendations, visualizing them, and offering explanations for the proposed POIs. The recommendation list is updated as any contextual factor changes, hence supporting the replanning of the visit.
Adaptive Data Processing and Analysis Techniques in eGovernment – ADAPTe
Conversational recommender systems support a structured human-computer interaction in order to assist online users in important online activities such as travel planning. In this project we have studied the effects and advantages of a novel recommendation methodology based on Machine Learning techniques (Reinforcement Learning) that allows conversational systems to autonomously improve an initial strategy in order to learn a new one that is more effective and efficient. We applied and tested our approach within a prototype of an online travel recommender system in collaboration with the Austrian Tourism portal (Austria.info).