In this contribution we review and discuss limits and chances of social recommender systems. After classifying and positioning social recommender systems in the basic landscape of recommender systems in general via a short review and comparison, we present related work in this more specialized area. After having laid out the basic conceptual grounds, we then contrast an earlier study with a recent study in order to investigate the limits of applicability of social recommenders. The earlier study replaces rating-similarity-based neighbourhoods in collaborative filtering with subgraphs of the user's social network (social filtering) and investigates the performance of the resulting classifier in a taste related domain. The other study which is discussed in more detail investigates the applicability of the method to recommendations of more factual, content-oriented items: posts in discussion boards. While the former study showed that the social filtering approach works very well in taste related domains, the second study shows that a mere transplantation of the idea to a more factual domain and a situation with sparse social network data does perform less satisfactorially. © Springer-Verlag Berlin Heidelberg 2012.
CITATION STYLE
Groh, G., Birnkammerer, S., & Köllhofer, V. (2012). Social Recommender systems. Intelligent Systems Reference Library, 32, 3–42. https://doi.org/10.1007/978-3-642-25694-3_1
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