Ramp-Up-Effects in Spatial Aware Recommender Systems

Stiller, Carsten; Roß, Fred; Ament, Christoph

doi:10.1109/icut.2009.5405724

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…In later stages the results of a recommender system according to equation 7can improve the results by adapting to user group behaviour. By further extending this approach by the extension to the first algorithm as proposed in [14], the Ramp-Up problems (as discussed in the following section) might further be extenuated.…”

Section: Discussionmentioning

confidence: 99%

Integration of Spatial User-Item Relations into Recommender Systems

Stiller¹,

Roß²,

Ament³

2010

IJI

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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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Section: Discussionmentioning

confidence: 99%

Integration of Spatial User-Item Relations into Recommender Systems

Stiller¹,

Roß²,

Ament³

2010

IJI

Self Cite

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Demographic recommendations for WEITBLICK, an assistance system for elderly

Stiller

Roß

Ament

2010

2010 10th International Symposium on Communications and Information Technologies

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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

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Ramp-Up-Effects in Spatial Aware Recommender Systems

Cited by 2 publications

References 10 publications

Integration of Spatial User-Item Relations into Recommender Systems

Integration of Spatial User-Item Relations into Recommender Systems

Demographic recommendations for WEITBLICK, an assistance system for elderly

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