Bogers, T. & van den Bosch, A.: Recommending scientific articles using citeulike. RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems. New York, NY, USA: ACM, 2008, S. 287-290
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Heymann, P.; Ramage, D. & Garcia-Molina, H.: Social tag prediction. SIGIR '08: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2008, S. 531-538
[Volltext]
In this paper, we look at the "social tag prediction" problem. Given a set of objects, and a set of tags applied to those objects by users, can we predict whether a given tag could/should be applied to a particular object? We investigated this question using one of the largest crawls of the social bookmarking system del.icio.us gathered to date. For URLs in del.icio.us, we predicted tags based on page text, anchor text, surrounding hosts, and other tags applied to the URL. We found an entropy-based metric which captures the generality of a particular tag and informs an analysis of how well that tag can be predicted. We also found that tag-based association rules can produce very high-precision predictions as well as giving deeper understanding into the relationships between tags. Our results have implications for both the study of tagging systems as potential information retrieval tools, and for the design of such systems.

Symeonidis, P.; Nanopoulos, A. & Manolopoulos, Y.: Tag recommendations based on tensor dimensionality reduction. RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems. New York, NY, USA: ACM, 2008, S. 43-50
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Vig, J.; Sen, S. & Riedl, J.: Tagsplanations: explaining recommendations using tags. IUI '09: Proceedingsc of the 13th international conference on Intelligent user interfaces. New York, NY, USA: ACM, 2008, S. 47-56
[Volltext]
While recommender systems tell users what items they might like, explanations of recommendations reveal why they might like them. Explanations provide many benefits, from improving user satisfaction to helping users make better decisions. This paper introduces tagsplanations, which are explanations based on community tags. Tagsplanations have two key components: tag relevance, the degree to which a tag describes an item, and tag preference, the user's sentiment toward a tag. We develop novel algorithms for estimating tag relevance and tag preference, and we conduct a user study exploring the roles of tag relevance and tag preference in promoting effective tagsplanations. We also examine which types of tags are most useful for tagsplanations.

Höhfeld, S. & Kwiatkowski, M.: Empfehlungssysteme aus informationswissenschaftlicher Sicht-State of the Art. In: IWP-Information Wissenschaft & Praxis 58 (2007), Nr. 5, S. 265-276
[Volltext]
Empfehlungssysteme tragen Inhalte individuell
Nutzer im WWW heran,
sierend auf deren konkreten Bedürfnissen,
rlieben und Interessen. Solche
steme können Produkte, Services,
tzer (mit analogen Interessen) uvm.
rschlagen und stellen damit – gerade
Web 2.0-Zeitalter – eine besondere
rm der Personalisierung sowie des
cial networking dar. Damit bieten
pfehlungssysteme Anbietern im ECommerce
nen entscheidenden
rktvorteil, weshalb die Auswertung
r Kundendaten bei großen Firmen
e Amazon, Google oder Ebay eine
he Priorität besitzt. Aus diesem
und wird im vorliegenden Artikel auf
e Ansätze von Empfehlungssystemen,
lche auf unterschiedliche Weise
e Bedürfnisse des Nutzers aufgreifen
w. „vorausahnen“ und ihm Vorschläge
us verschiedenen Bereichen)
terbreiten können, eingegangen. Der
tikel liefert eine Definition und Darstellung
r Arbeitsweisen von Empfehlungssystemen.
bei werden die
rschiedenen Methodiken jener
enste vergleichend erläutert, um ihre
weiligen Vor- und Nachteile deutlich
machen. Außerdem wird der Ontologie-
d Folksonomy-Einsatz innerhalb
n Empfehlungssystemen beleuchtet,
Chancen und Risiken der Anwendung
n Methoden der Wissensrepräsentation
r zukünftige Forschungsarbeiten
nschätzen zu können.
commender Systems in an Information
ience View – The State of the Art
commender systems offer content
dividually to users in the WWW,
sed on their concrete needs, preferences
d interests. Those systems
n propose products, services, users
ith analogous interests), etc.) and
present a special form of personalisation
well as of social networking
exactly in the Web 2.0 age. Recommender
stems offer e.g. suppliers in
e e-commerce a crucial market advantage.
, the evaluation of the customer
ta has high priority at big
mpanies like Amazon, Google or
ay. For this reason we engaged in
commender systems, which take up
e user’s needs in different ways, to
nticipate“ needs and make suggestions
rom different areas) to the user.
is review article achieves a definition
d representation of operations
d methods of recommender systems.
actly the different methodologies
those services should be expounded
mparativly on that occasion
order to represent advantages
d disadvantages. The use of ontologies
d folksonomies as implementations
recommender systems is portrayed
order to be able to take into
nsideration chances and risks of the
plication of knowledge representation
thods for future researches.

Niwa, S.; Doi, T. & Honiden, S.: Web Page Recommender System based on Folksonomy Mining. ITNG '06: Proceedings of the Third International Conference on Information Technology: New Generations (ITNG'06). Washington, DC, USA: IEEE Computer Society, 2006, S. 388-393
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Xu, Y.; Zhang, L. & Liu, W.: Cubic Analysis of Social Bookmarking for Personalized Recommendation. In: Frontiers of WWW Research and Development - APWeb 2006 (2006), S. 733-738
[Volltext]
Personalized recommendation is used to conquer the information overload problem, and collaborative filtering recommendation (CF) is one of the most successful recommendation techniques to date. However, CF becomes less effective when users have multiple interests, because users have similar taste in one aspect may behave quite different in other aspects. Information got from social bookmarking websites not only tells what a user likes, but also why he or she likes it. This paper proposes a division algorithm and a CubeSVD algorithm to analysis this information, distill the interrelations between different users’ various interests, and make better personalized recommendation based on them. Experiment reveals the superiority of our method over traditional CF methods. ER -