In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of
user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.
ER - TY - CONF AU - Krause, Beate AU - Jäschke, Robert AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Logsonomy - Social Information Retrieval with Logdata T2 - HT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia PB - ACM C1 - New York, NY, USA PY - 2008/ CY - VL - IS - SP - 157 EP - 166 UR - http://portal.acm.org/citation.cfm?id=1379092.1379123&coll=ACM&dl=ACM&type=series&idx=SERIES399&part=series&WantType=Journals&title=Proceedings%20of%20the%20nineteenth%20ACM%20conference%20on%20Hypertext%20and%20hypermedia DO - http://doi.acm.org/10.1145/1379092.1379123 KW - 2.0 KW - 2008 KW - analysis KW - folksonomy KW - information KW - itegpub KW - logsonomy KW - myown KW - network KW - retrieval KW - search KW - social KW - tagorapub KW - web KW - web2.0 KW - web20 L1 - SN - 978-1-59593-985-2 N1 - N1 - AB - Social bookmarking systems constitute an established
part of the Web 2.0. In such systems
users describe bookmarks by keywords
called tags. The structure behind these social
systems, called folksonomies, can be viewed
as a tripartite hypergraph of user, tag and resource
nodes. This underlying network shows
specific structural properties that explain its
growth and the possibility of serendipitous
exploration.
Today’s search engines represent the gateway
to retrieve information from the World Wide
Web. Short queries typically consisting of
two to three words describe a user’s information
need. In response to the displayed
results of the search engine, users click on
the links of the result page as they expect
the answer to be of relevance.
This clickdata can be represented as a folksonomy
in which queries are descriptions of
clicked URLs. The resulting network structure,
which we will term logsonomy is very
similar to the one of folksonomies. In order
to find out about its properties, we analyze
the topological characteristics of the tripartite
hypergraph of queries, users and bookmarks
on a large snapshot of del.icio.us and
on query logs of two large search engines.
All of the three datasets show small world
properties. The tagging behavior of users,
which is explained by preferential attachment
of the tags in social bookmark systems, is
reflected in the distribution of single query
words in search engines. We can conclude
that the clicking behaviour of search engine
users based on the displayed search results
and the tagging behaviour of social bookmarking
users is driven by similar dynamics. ER - TY - CONF AU - Krause, Beate AU - Schmitz, Christoph AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems T2 - Proc. of the Fourth International Workshop on Adversarial Information Retrieval on the Web PB - C1 - PY - 2008/ CY - VL - IS - SP - EP - UR - http://airweb.cse.lehigh.edu/2008/submissions/krause_2008_anti_social_tagger.pdf DO - KW - 2.0 KW - 2008 KW - bookmarking KW - folksonomies KW - folksonomy KW - itegpub KW - myown KW - social KW - spam KW - systems KW - tagger KW - tagorapub KW - web KW - web2.0 L1 - SN - N1 - N1 - AB - ER -