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People increasingly use social networks to manage various aspects of their lives
such as communication, collaboration, and information sharing. A user’s network
of friends may offer a wide range of important benefits such as receiving online
help and support and the ability to exploit professional opportunities. One of the
most profound properties of social networks is their dynamic nature governed by
people constantly joining and leaving the social networks. The circle of friends may
frequently change when people establish friendship through social links or when
their interest in a social relationship ends and the link is removed.
This book introduces novel techniques and algorithms for social network-based
recommender systems. Here, concepts such as link prediction using graph patterns,
following recommendation based on user authority, strategic partner selection
in collaborative systems, and network formation based on “social brokers” are
presented. In this book, well-established graph models such as the notion of hubs
and authorities provide the basis for authority-based recommendation and are
systematically extended towards a unified Hyperlink Induced Topic Search (HITS)
and personalized PageRank model. Detailed experiments using various real-world
datasets and systematic evaluation of recommendation results proof the applicability
of the presented concepts.