Demographic recommendations for WEITBLICK, an assistance system for elderly

This paper evaluates the possible usage of demographic recommender systems for an assistance system called WEITBLICK. The aim of WEITBLICK is to provide elderly with information about services from the areas care, health, recreation, household, etc. Three types of demographic recommender systems are studied. All of them use linear predictors to make assumptions about unknown ratings of items by the users. The predictors are learned by gradient descent (GD), exponentiated gradient descent (EG), and exponentiated gradient descent with positive and negative weights (EG^±). Using a data set from a user survey, it is shown that EG and EG^± perform best. Furthermore, a way to reduce computing time while only trading in a reasonable amount of accuracy is explained. A discussion about the usage of the results for further research is provided.