TY - CHAP
T1 - Social Recommender systems
AU - Groh, Georg
AU - Birnkammerer, Stefan
AU - Köllhofer, Valeria
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Collaborative Filtering
KW - Social Context
KW - Social Recommender Systems
UR - http://www.scopus.com/inward/record.url?scp=84885622310&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-25694-3_1
DO - 10.1007/978-3-642-25694-3_1
M3 - Chapter
AN - SCOPUS:84885622310
SN - 9783642256936
T3 - Intelligent Systems Reference Library
SP - 3
EP - 42
BT - Recommender Systems for the SocialWeb
A2 - Pazos Arias, Jose
A2 - Redondo, Rebeca Daz
A2 - Vilas, Ana Fernandez
ER -