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Abstract:
Online communities like Flickr, del.icio.us and YouTube have established
themselves as very popular and powerful services for publishing
and searching contents, but also for identifying other users
who share similar interests. In these communities, data is usually
annotated with carefully selected and often semantically meaningful
tags, collaboratively chosen by the user who uploaded an
item and other users who came across the item. Items like urls or
videos are typically retrieved by issueing queries that consist of a
set of tags, returning items that have been frequently annotated with
these tags. However, users often prefer a more personalized way of
searching over such a ‘global’ search, exploiting preferences of and
connections between users.
The SENSE system presented in this demo supports hybrid personalization
along two dimensions: in the social dimension, a search
process is focused towards items tagged by users explicitly selected
as friends by the querying user, whereas in the spiritual dimension,
users that share preferences with the querying user are preferred.
Orthorgonal to this, the system additionally integrates semantic expansion
of query tags to improve search results. SENSE provides
an efficient top-k algorithm that dynamically expands the search to
related users and tags. It is based on principles of threshold algorithms,
folding related users and tags into the search space in an
incremental on-demand manner, thus visiting only a small fraction
of the social network when evaluating a query. The demonstration
uses three different real-world datasets: A large set of urls from
del.icio.us, a large set of pictures from Flickr, and a large set of
books from librarything, each together with a large fraction of the
corresponding social network of these sites.