Explaining and Combining Recommender Algorithms for Decen- tralized Architectures (ENCORE)

Collaborative filtering is a new method for retrieving information. Instead of basing the search on the content in the information units, a collaborative filter or recommender system uses databases of user ratings as a basis for predictions. From our user studies we see that collaborative filtering can be of great help to users when they search for information. In order to be truly useful collaborative filtering needs more research. Specifically, we have seen that it can be improved by exploring new ways of explaining to users why they get a particular recommendation. Secondly, for some application scenarios, collaborative filtering has to work in networks where there is no central server – thus requiring a decentralized recommender model. Finally, it is necessary to find ways of combining traditional content-based methods for information filtering with collaborative information to improve the filtering process and solve some of the problems with collaborative filtering.