Managing Social Wisdom: Efficient and Effective Search in Social Tagging Networks

content, as well as for finding and connecting to other users.
User-generated content includes, for example, personal blogs, bookmarks,
and digital photos. These items can be annotated and rated
by different users, and these social tags and derived user-specific
scores can be leveraged for searching relevant content and discovering
subjectively interesting items. Moreover, the relationships
among users can also be taken into consideration for ranking search
results, the intuition being that you trust the recommendations of
your close friends more than those of your casual acquaintances.

Queries for tag or keyword combinations that compute and rank
the top-k results thus face a large variety of options that complicate
the query processing and pose efficiency challenges.
We present an incremental top-k algorithm that addresses these issues
with two-dimensional expansions: social expansion considers the
strength of relations among users, and semantic expansion considers
the relatedness of different tags. Based on principles of threshold
algorithms, it folds friends and related tags into the search space in
an incremental on-demand manner. The excellent performance of the
method is demonstrated by an experimental evaluation on real-world datasets.