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Graph-based recommendation in broad folksonomies [Elektronische Ressource] / vorgelegt von Robert Wetzker

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220 pages
Graph-Based Recommendation inBroad Folksonomiesvorgelegt vonDiplom-IngenieurRobert WetzkerVon der Fakult at IV { Elektrotechnik und Informatik {der Technischen Universit at Berlinzur Erlangung des akademischen GradesDoktor der Ingenieurwissenschaften{ Dr.-Ing. {genehmigte DissertationPromotionsausschuss:Vorsitzender: Prof. Dr. M ollerBerichter: Prof. Dr. AlbayrakBerichter: Prof. Dr. BauckhageTag der wissenschaftlichen Aussprache: 14. Mai 2010Berlin 2010D 83AbstractThe user-centric annotation of resources by freely chosen words (tags) hasbecome the predominant form of content categorization of the Web 2.0 age.It provided the foundations for the success of now famous services, suchas Delicious, Flickr, LibraryThing, or Last.fm. Tags have proven to be apowerful alternative to existing top-down categorization techniques, as forexample taxonomies or prede ned dictionaries, which lack exibility and areexpensive in their creation and maintenance. Instead, tagging allows usersto choose the labels that match their real needs, tastes, or language, whichreduces the required cognitive e ort.Services that center on the collaborative tagging of resources are calledfolksonomies. These communities have become an invaluable source for infor-mation retrieval, since they bundle the interests of thousand or even millionsof users, magnifying the underlying domain from a user-centric perspective.
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Graph-Based Recommendation in
Broad Folksonomies
vorgelegt von
Diplom-Ingenieur
Robert Wetzker
Von der Fakult at IV { Elektrotechnik und Informatik {
der Technischen Universit at Berlin
zur Erlangung des akademischen Grades
Doktor der Ingenieurwissenschaften
{ Dr.-Ing. {
genehmigte Dissertation
Promotionsausschuss:
Vorsitzender: Prof. Dr. M oller
Berichter: Prof. Dr. Albayrak
Berichter: Prof. Dr. Bauckhage
Tag der wissenschaftlichen Aussprache: 14. Mai 2010
Berlin 2010
D 83Abstract
The user-centric annotation of resources by freely chosen words (tags) has
become the predominant form of content categorization of the Web 2.0 age.
It provided the foundations for the success of now famous services, such
as Delicious, Flickr, LibraryThing, or Last.fm. Tags have proven to be a
powerful alternative to existing top-down categorization techniques, as for
example taxonomies or prede ned dictionaries, which lack exibility and are
expensive in their creation and maintenance. Instead, tagging allows users
to choose the labels that match their real needs, tastes, or language, which
reduces the required cognitive e ort.
Services that center on the collaborative tagging of resources are called
folksonomies. These communities have become an invaluable source for
information retrieval, since they bundle the interests of thousand or even millions
of users, magnifying the underlying domain from a user-centric perspective.
Furthermore, the existence of tags allows for the uni ed modeling of content
independent of its type or format and without the need of expensive
content retrieval, processing, storage, or indexing. Of special interest from an
information retrieval perspective are the tags that are generated in \broad
folksonomies", where many users reference and tag the same objects. This
collaborative tagging produces high quality content descriptors and
characteristic tag spectra with high potential for better content models.
This dissertation aims to develop novel algorithms for the personalized
recommendation of objects within folksonomies. Recommender systems
support users in the discovery of valuable content out of an overwhelming set of
choices and already increase the perceived value of many web applications.
However, the tripartite structure of folksonomies that results from the
interaction of content, tags, and users goes beyond the capability of common
recommendation techniques. The algorithms presented in this dissertation
are instead especially tailored towards to needs of folksonomies. They
incorporate tagging and usage patterns in parallel in order to derive more precise
and robust recommendation models. Exhaustive tests for di erent settings
and various folksonomies show that the developed solutions produce more
iiiaccurate recommendations than the current state-of-the-art.
ivZusammenfassung
Die nutzerzentrierte Klassi kation von Ressourcen durch Zuweisung frei
w ahlbarer W orter (Tags) hat sich zu einem integralen Bestandteil moderner
Webanwendungen entwickelt. W ahrend sich kontrollierte Klassi
kationsschemata wie etwa Taxonomien bzw. Ontologien aus Nutzersicht h au g
als zu starr, kostspielig und wenig dynamisch erweisen, gew ahren Tags ein
hohes Ma an Flexibilit at und erfordern nur geringen kognitiven Aufwand
w ahrend des Klassi kationssprozesses. Die Nutzerfreundlichkeit und E
ektivit at dieser Art der unkontrollierten Inhaltsklassi kation begrundete den
Erfolg und die heutige weite Verbreitung von Tagging Communities wie
Delicious, Flickr, LibraryThing oder Last.fm, welche man auch als Folksonomien
bezeichnet. Folksonomien bundeln die Interessen von Tausenden oder
Millionen von Nutzern und werden dadurch zu einzigartigen Informationsquellen.
Die Existenz von Tags erlaubt darub er hinaus eine einheitliche Modellierung
von Inhalten unabh angig von ihrer Art oder ihres Formats und erspart in
vielen F allen eine aufwendige Verarbeitung, Speicherung sowie Indizierung der
Inhalte selbst. Aus Datenverarbeitungssicht besonders interessant sind
sogenannte \broad folksonomies", bei welchen die selben Inhalte von mehreren
Nutzern referenziert und annotiert werden k onnen, so dass es zur Bildung
deskriptiver und charakteristischer Worth aufungen kommt.
In dieser Dissertation werden Algorithmen zur personalisierten
Empfehlung von Inhalten in Folksonomien entworfen und evaluiert.
Empfehlungssysteme unterstutzen Nutzer beim Entdecken interessanter
Inhalte aus einer unub ersichtlichen Menge existierender M oglichkeiten.
Obwohl bereits umfangreiche Forschungsarbeiten auf dem Gebiet von
Empfehlungssystemen existieren, lassen sich diese nicht ohne
Informationsverlust auf die tripartite Folksonomiestruktur aus Inhalten, Nutzern und
Tags ub ertragen. Die in dieser Arbeit entwickelten Algorithmen beruc
ksichtigen hingegen diese besondere Graphstruktur und pro tieren von den durch
Tags gegebenen semantischen Zusatzinformationen. Ausfuhrlic he Tests in
verschiedenen Szenarien und fur mehrere Folksonomien zeigen, dass die
entwickelten L osungen bessere Empfehlungen liefern als existierende Ans atze.
vAcknowledgments
This dissertation would not have been possible without the constant
encouragement and support of many inspiring people.
I was fortunate to have two experienced supervisors during my time at the
DAI-Labor of the Technische Universit at Berlin. Professor Sahin Albayrak
convinced me to follow the academic career path and provided the guidance
as well as the freedom that were essential for this dissertation. Professor
Christian Bauckhage introduced me to the world of research and has been
an enduring source of inspiration and assistance ever since.
Furthermore, I have been lucky to work with an amazing group of
researchers during the past four years. I am deeply thankful to: Tansu Alpcan,
Leonhard Hennig, Alexander Korth, Florian Metze, Nicolas Neubauer, Till
Plumbaum, Alan Said, Winfried Umbrath, and Carsten Zimmermann.
I would especially like to thank Tansu Alpcan, Florian Metze, and
Christian Bauckhage who introduced me to the topic of folksonomies and provided
the opportunity for extensive study in this direction. I thank Winfried
Umbrath and Alan Said for all the fruitful discussions and countless MATLAB
nights. I also want to thank Jer^ ome Kunegis, who constantly challenged the
problems and solutions we discussed. Trying to convince him of my ideas
always contested previous believes and led to new insights. I further thank
all other members of the competence center on information retrieval and
machine learning (CC-IRML) as well as the entire team of the DAI-Labor for
their collaboration and support during the last years.
Finally, I would like to express my deep gratitude to the many people who
proofread this dissertation: Leonhard Hennig, Alan Said, Andreas Hotho,
Alice Motanga, Carsten Zimmermann, Stefan Fricke, and Jer^ ome Kunegis.
Their valuable comments, questions, and ideas have helped to improve the
quality and coherence of this dissertation signi cantly.
Last but not least I thank my family. For everything.
viiPublications
Many of the materials and concepts in this thesis have appeared in previous
publications by the author.
Robert Wetzker, Carsten Zimmermann, Christian Bauckhage, Sahin
Albayrak, I tag, You tag: Translating Tags for Advanced User Models,
In: 3rd ACM Int. Conf. on Web Search and Data Mining (WSDM),
2010
Alan Said, Robert Wetzker, Winfried Umbrath, Leonhard Hennig, A
hybrid PLSA approach for warmer cold start in folksonomy
recommendation, In: 3rd ACM Conf. on Recommender Systems, Workshop on
Recommender Systems and the Social Web, 2009
Robert Wetzker, Alan Said, Carsten Zimmermann, Understanding the
user: Personomy translation for tag recommendation, In: Eur. Conf.
on Machine Learning and Principles and Practice of Knowledge
Discovery in Databases (ECML PKDD), Discovery Challenge, 2009
Nicolas Neubauer, Robert Wetzker, Klaus Obermayer, Tag Spam
Creates Large Non-Giant Connected Components, In: 18th Int. World
Wide Web Conference, 5th Int. Workshop on Adversarial Information
Retrieval on the Web (AIRWEB), 2009
Robert Wetzker, Winfried Umbrath, Alan Said, A hybrid approach to
item recommendation in folksonomies (Best Technical Paper), In: 2nd
ACM Int. Conf. on Web Search and Data Mining (WSDM), Workshop
on Exploiting Semantic Annotations in Information Retrieval (ESAIR),
2009
Robert Wetzker, Carsten Zimmermann, Christian Bauckhage,
Detecting Trends in Social Bookmarking Systems: A del.icio.us Endeavor, In:
Int. Journal of Data Warehousing and Mining (to appear)
ix Christian Bauckhage, Tansu Alpcan, Robert Wetzker, Winfried
Umbrath, IMAGE RETRIEVAL AND WEB 2.0: WHERE CAN WE GO
FROM HERE?, In: 15th IEEE Int. Conf. on Image Processing (ICIP),
2008
Robert Wetzker, Till Plumbaum, Alexander Korth, Christian
Bauckhage, Tansu Alpcan, Florian Metze, Detecting Trends in Social
Bookmarking Systems using a Probabilistic Generative Model and
Smoothing, In: Int. Conf. on Pattern Recognition (ICPR), 2008
Robert Wetzker, Carsten Zimmermann, Christian Bauckhage,
Analyzing Social Bookmarking Systems: A del.icio.us Cookbook, In: Eur.
Conf. on Arti cial Intelligence (ECAI), 2008
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