ACM SIGIR Forum
Identifying similarities, periodicities and bursts for online search queries
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Semantic similarity between search engine queries using temporal correlation
WWW '05 Proceedings of the 14th international conference on World Wide Web
Usage patterns of collaborative tagging systems
Journal of Information Science
InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
Methods for comparing rankings of search engine results
Computer Networks: The International Journal of Computer and Telecommunications Networking - Web dynamics
Why we search: visualizing and predicting user behavior
Proceedings of the 16th international conference on World Wide Web
Optimizing web search using social annotations
Proceedings of the 16th international conference on World Wide Web
Can social bookmarking enhance search in the web?
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Purpose tagging: capturing user intent to assist goal-oriented social search
Proceedings of the 2008 ACM workshop on Search in social media
TC-SocialRank: Ranking the Social Web
WAW '09 Proceedings of the 6th International Workshop on Algorithms and Models for the Web-Graph
The TicTag application: towards tag-based meta-search for browsing the web
Proceedings of the 23rd British HCI Group Annual Conference on People and Computers: Celebrating People and Technology
A brief survey of computational approaches in social computing
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
I tag, you tag: translating tags for advanced user models
Proceedings of the third ACM international conference on Web search and data mining
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Context comparison of bursty events in web search and online media
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Find, new, copy, web, page - tagging for the (re-)discovery of web pages
TPDL'11 Proceedings of the 15th international conference on Theory and practice of digital libraries: research and advanced technology for digital libraries
Folksonomy query suggestion via users' search intent prediction
FQAS'11 Proceedings of the 9th international conference on Flexible Query Answering Systems
A quadratic approach for trend detection in folksonomies
RR'12 Proceedings of the 6th international conference on Web Reasoning and Rule Systems
Sopra: a new social personalized ranking function for improving web search
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Evaluation of personalized social ranking functions of information retrieval
ICWE'13 Proceedings of the 13th international conference on Web Engineering
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Social bookmarking systems allow users to store links to internet resources on a web page. As social bookmarking systems are growing in popularity, search algorithms have been developed that transfer the idea of link-based rankings in the Web to a social bookmarking system's data structure. These rankings differ from traditional search engine rankings in that they incorporate the rating of users. In this study, we compare search in social bookmarking systems with traditionalWeb search. In the first part, we compare the user activity and behaviour in both kinds of systems, as well as the overlap of the underlying sets of URLs. In the second part,we compare graph-based and vector space rankings for social bookmarking systems with commercial search engine rankings. Our experiments are performed on data of the social bookmarking system Del.icio.us and on rankings and log data from Google, MSN, and AOL. We will show that part of the difference between the systems is due to different behaviour (e. g., the concatenation of multi-word lexems to single terms in Del.icio.us), and that real-world events may trigger similar behaviour in both kinds of systems. We will also show that a graph-based ranking approach on folksonomies yields results that are closer to the rankings of the commercial search engines than vector space retrieval, and that the correlation is high in particular for the domains that are well covered by the social bookmarking system.