Contextualization, user modeling and personalization in the social web [Elektronische Ressource] : from social tagging via context to cross-system user modeling and personalization / Fabian Abel

Fabian Abel

Sujets

• Informatik

Informations

• Publié par: gottfried_wilhelm_leibniz_universitat_hannover
• Publié le: 01 janvier 2011
• Nombre de lectures: 18
• Langue: English
• Poids de l'ouvrage: 12 Mo

Extrait

Contextualization, User Modeling and
Personalization in the Social Web
From Social Tagging via Context to Cross-System User
Modeling and Personalization
Von der Fakultat fur Elektrotechnik und Informatik
der Gottfried Wilhelm Leibniz Universitat Hannover
zur Erlangung des Grades
Doktor der Naturwissenschaften
Dr. rer. nat.
genehmigte Dissertation
von
Fabian Abel
geboren am 8. Juli 1980 in Hannover, Deutschland
2011Kommission:
Referentin: Prof. Dr. Nicola Henze
Korreferent: Prof. Dr. Wolfgang Nejdltin: Prof. Dr. Cristina Baroglio
Tag der Promotion: 20. April 2011Abstract
Social Web stands for the culture of participation and collaboration on the Web. Struc-
tures emerge from social interactions: social tagging enables a community of users to
assign freely chosen keywords to Web resources. The structure that evolves from so-
cial tagging is called folksonomy and recent research has shown that the exploitation of
folksonomy structures is bene cial to information systems.
In this thesis we propose models that better capture usage context of social tagging and
develop two folksonomy systems that allow for the deduction of contextual information
from tagging activities. We introduce a suite of ranking algorithms that exploit con-
textual information embedded in folksonomy structures and prove that these context-
sensitive ranking algorithms signi cantly improve search in Social Web systems. We
setup a framework of user modeling and personalization methods for the Social Web
and evaluate this framework in the scope of personalized search and social recommender
systems. Extensive evaluation reveals that our context-based user modeling techniques
have signi cant impact on the personalization quality and clearly improve regular user
modeling approaches. Finally, we analyze the nature of user pro les distributed on the
Social Web, implement a service that supports cross-system user modeling and investi-
gate the impact of cross-system user modeling methods on personalization. In di erent
experiments we prove that our cross-system user modeling strategies solve cold-start
problems in social recommender systems and that intelligent re-use of external pro le
information improves the recommendation quality also beyond the cold-start.
Keywords: user modeling, personalization, social webAbstract
Das Social Web beschreibt eine Kultur der Partizipation, in der Internetbenutzer durch
ihre Beitrage selbst zu einem wichtigen Bestandteil des World Wide Web werden. Im
Social Web entstehen Strukturen durch soziale Interaktionen. So werden beim Social
Tagging Web Ressourcen von einer Gruppe von Benutzern gemeinsam beschlagwortet.
Das Resultat dieses emergenten Prozesses sind sogenannte Folksonomien, die Benutzer,
Web Ressourcen und Schlagworter (Tags) miteinander in Relation setzen. Verwandte
Arbeiten haben gezeigt, dass Folksonomien vorteilhaft in Informationssystemen genutzt
werden konnen, um etwa Suche zu verbessern oder benutzerspezi sche Empfehlungen
zu generieren.
In dieser Arbeit werden Modelle und Methoden eingefuhrt, die den Kontext von So-
cial Tagging besser erfassen. Diese Methoden werden in zwei Onlinesystemen demon-
striert, die wir im Rahmen dieser Arbeit entwickelt haben. Ferner stellen wir eine
Reihe von Ranking Algorithmen vor, die Kontextinformation dazu verwenden um El-
emente entsprechend anwendungs- und benutzerspezi schen Relevanzkriterien zu ord-
nen. Unsere Experimente zeigen, dass diese kontextsensitiven Algorithmen Suche in
Social Tagging Systemen signi kant verbessern. Zudem stellen wir Methoden zur kon-
textbasierten Benutzermodellierung vor und zeigen, dass unsere Methoden erfolgreich
fur die Personalisierung von Social Web Systemen eingesetzt werden konnen. Un-
sere kontextbasierten Ansatze fuhren im Vergleich zu herkommlichen Benutzermodel-
lierungsstrategien zu einer signi kanten Verbesserung von personalisierter Suche und
Empfehlungsfunktionalitat. Schlie lich untersuchen wir wie Benutzermodellierung im
Social Web uber Systemgrenzen hinaus umgesetzt werden kann. Hierzu analysieren wir
die Charakteristiken von Pro ldaten, die ub er verschiedene Social Web Systeme verteilt
sind, implementieren ein Framework zur Unterstutzung von systemub ergreifender Be-
nutzermodellierung und erforschen welchen Ein uss system ubergreifende Benutzermod-
ellierung auf Personalisierung in Social Web Systemen hat. Unsere Ergebnisse beweisen,
dass unsere Benutzermodellierungsstrategien Kaltstartprobleme in Systemen losen, die
an den Benutzer angepasste Empfehlungen bereitstellen wollen, und ferner Personal-
isierung uber den Kaltstart hinaus signi kant verbessern.
Schlagworte: Benutzermodellierung, Personalisierung, Social WebForeword
In the last years I published the building blocks of this thesis in several workshops,
conferences, journals and book chapters relevant to the research area of information
systems. Here, I list the most important publications that directly contribute to my
thesis.
Basic principles and models that build the basis for our algorithms are best described
in the following publications.
The Bene t of additional Semantics in Folksonomy Systems. By F. Abel. In
Proceedings of the 2nd PhD Workshop on Information and Knowledge Management
(PIKM ’08), ACM, 2008 [1].
Social Semantic Web at work: annotating and grouping Social Media content.
By F. Abel, N. Henze, and D. Krause. In S. H. Jose Cordeiro and J. Filipe,
editors, Web Information Systems and Technologies, Lecture Notes in Business
Information Processing, volume 18, Springer, 2009 [25].
Semantic Enhancement of Social Tagging Systems. By F. Abel, N. Henze, D. Krause,
and M. Kriesell. In Vladan Devedzic, Dragan Gasevic, editors, Annals of Infor-
mation Systems { Web 2.0 & Semantic Web, volume 6, 2009 [28].
Multi-faceted Tagging in TagMe!. By F. Abel, R. Kawase, D. Krause, and P. Siehn-
del. In 8th International Semantic Web Conference (ISWC ’09), Springer, 2009 [35].
We implemented these principles and and approaches to user and context modeling in
di erent systems. We developed GroupMe!, a social bookmarking system that enables
users to visually organize their bookmarks in groups, and TagMe!, a tagging and explo-
ration front-end for Flickr images. Further, we implemented the so-called Grapple User
Modeling Framework (GUMF), which allows for user modeling across system bound-
aries, and the Mypes service, which is part of GUMF and provides functionality for
aggregating and aligning user data distributed across the Social Web. These tools have,
for example, been presented in the subsequent research articles.
GroupMe! { Where Semantic Web meets Web 2.0. By F. Abel, M. Frank,
N. Henze, D. Krause, D. Plappert, and P. Siehndel. In 6th International Semantic
Web Conference (ISWC ’07), Springer, 2007 [10].
A Novel Approach to Social Tagging: GroupMe!. By F. Abel, N. Henze, and
D. Krause. In 4th International Conference on Web Information Systems and
iii
Technologies (WEBIST), INSTICC Press, 2008 [22].
GroupMe! - Where Information meets. By F. Abel, N. Henze, and D. Krause.
In Proceedings of the 17th International Conference on World Wide Web (WWW
’08), ACM, 2008 [21].
GroupMe! - Combining ideas of Wikis, Social Bookmarking, and Blogging. By
F. Abel, M. Frank, N. Henze, D. Krause, and P. Siehndel. In 2nd International
Conference on Weblogs and Social Media (ICWSM 2008), AAAI Press, 2008 [12].
The Art of multi-faceted Tagging { interweaving spatial annotations, categories,
meaningful URIs and tags. By F. Abel, R. Kawase, D. Krause, P. Siehndel, and
N. Ullmann. In 6th International Conference on Web Information Systems and
Technologies (WEBIST ’10), INSTICC Press, 2010 [36].
Mashing up user data in the Grapple User Modeling Framework. By F. Abel,
D. Heckmann, E. Herder, J. Hidders, G.-J. Houben, D. Krause, E. Leonardi, and
K. van der Slujis. In Workshop on Adaptivity and User Modeling in Interactive
Systems (ABIS ’09), 2009 [14].
The systems and tools we implemented served as playground to experiment with the
algorithms, which we outline in this thesis. For example, we introduce several algo-
rithms that exploit contextual information embedded in social tagging structures and
apply these algorithms for search and ranking in tagging systems. An overview of these
algorithms and corresponding evaluations regarding search and ranking in social tagging
systems is given in the following papers.
On the e ect of group structures on ranking strategies in folksonomies. By F. Abel,
N. Henze, D. Krause, and M. Kriesell. In R. Baeza-Yates and I. King, editors,
Weaving Services and People on the World Wide Web, Springer, 2009 [27].
Ranking in Folksonomy Systems: can context help? By F. Abel, N. Henze, and
D. Krause. In Proceedings of the 17th ACM Conference on Information and Knowl-
edge Management (CIKM ’08), ACM, 2008 [23].
Context-aware ranking algorithms in folksonomies. By F. Abel, N. Henze, and
D. Krause. In Proceedings of the Fifth International Conference on Web Informa-
tion Systems and Technologies (WEBIST ’09), INSTICC Press, 2009 [24].
Optimizing search and ranking in folksonomy systems by exploiting context in-
formation. By F. Abel, N. Henze, and D. Krause. Lecture Notes in Business
Information Processing, volume 45(2), Springer, 2010 [26].
The impact of multifaceted tagging on learnin