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
Recommender systems aggregate information about users and items to be recommended to generate adequate recommendations. This paper proposes two approaches to include information about spatial releationships of users and items in order to improve the quality of recommendations. The two approaches are compared with non-spatial recommendation using a set of evaluation metrics. Special ramp-up problems, occurring when including spatial information into recommender systems and different application areas for spatial recommender systems are discussed.
The changing demography requires new kinds of support for elderly people. The project WEITBLICK aims to give seniors assistance to gather information about several services and their providers, relaying the access to such services and offer them in an individualized manner. To determine the requirements of elderly users a broad analysis will be performed in four stages. To fulfill the aims of the project the system has two principles incorporated: the service relay can be triggered by the users’ former activities or by the users actively themselves. The base for both is a database with user and service profiles
Recommender systems aggregate information about users and items to be recommended to generate adequate recommendations. This paper proposes two approaches to include information about spatial relationships of users and items in order to improve the quality of recommendations. The two approaches are compared with non-spatial recommendation using two synthesized data sets and four different evaluation metrics. The results show how spatial information can improve recommender results. Furthermore each of the two approaches can perform better than the other, depending on what assumption of user behaviour is represented by the data set. Special ramp-up problems, occurring when including spatial information into recommender systems and different application areas for spatial recommender systems are discussed.
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