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.
%0 Book Section
%1 citeulike:10857757
%A Groh, Georg
%A Birnkammerer, Stefan
%A Köllhofer, Valeria
%B Recommender Systems for the Social Web
%C Berlin, Heidelberg
%D 2012
%I Springer Berlin Heidelberg
%K dlpaws recommender review social-network
%P 3--42
%R 10.1007/978-3-642-25694-3_1
%T Social Recommender Systems
%U http://dx.doi.org/10.1007/978-3-642-25694-3_1
%V 32
%X 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.
%& 1
%@ 978-3-642-25693-6
@incollection{citeulike:10857757,
abstract = {{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.}},
added-at = {2018-03-19T12:24:51.000+0100},
address = {Berlin, Heidelberg},
author = {Groh, Georg and Birnkammerer, Stefan and K\"{o}llhofer, Valeria},
biburl = {https://www.bibsonomy.org/bibtex/27301e6507929574b40a9f11e45090d81/aho},
booktitle = {Recommender Systems for the Social Web},
chapter = 1,
citeulike-article-id = {10857757},
citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-642-25694-3_1},
citeulike-linkout-1 = {http://www.springerlink.com/content/ql42412262072864},
doi = {10.1007/978-3-642-25694-3_1},
interhash = {2fad8c201f6c8c47ce4bfb414dcf1ee1},
intrahash = {7301e6507929574b40a9f11e45090d81},
isbn = {978-3-642-25693-6},
keywords = {dlpaws recommender review social-network},
pages = {3--42},
posted-at = {2012-07-05 02:02:48},
priority = {2},
publisher = {Springer Berlin Heidelberg},
series = {Intelligent Systems Reference Library},
timestamp = {2018-03-19T12:24:51.000+0100},
title = {{Social Recommender Systems}},
url = {http://dx.doi.org/10.1007/978-3-642-25694-3_1},
volume = 32,
year = 2012
}