Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A large-scale evaluation and analysis of personalized search strategies
Proceedings of the 16th international conference on World Wide Web
Can social bookmarking improve web search?
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Exploring folksonomy for personalized search
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Efficient top-k querying over social-tagging networks
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Making SENSE: socially enhanced search and exploration
Proceedings of the VLDB Endowment
Building community-centric information exploration applications on social content sites
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
On social networks and collaborative recommendation
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Personalized social search based on the user's social network
Proceedings of the 18th ACM conference on Information and knowledge management
CubeLSI: An effective and efficient method for searching resources in social tagging systems
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Context-aware top-K processing using views
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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We demonstrate the Taagle system for top-k retrieval in social tagging systems (also known as folksonomies). The general setting is the following: users form a weighted social network, which may reflect friendship, similarity, or trust; items from a public pool of items (e.g., URLs, blogs, photos, documents) are tagged by users with keywords; users search for the top-k items having certain tags. Going beyond a classic search paradigm where data is decoupled from the users querying it, users can now act both as producers and seekers of information. Hence finding the most relevant items in response to a query should be done in a network-aware manner: items tagged by users who are closer (more similar) to the seeker should be given more weight than items tagged by distant users. We illustrate with Taagle novel algorithms and a general approach that has the potential to scale to current applications, in an online context where the social network, the tagging data and even the seekers' search ingredients can change at any moment. We also illustrate possible design choices for providing users a fully-personalized and customizable search interface. By this interface, they can calibrate how social proximity is computed (for example, with respect to similarity in tagging actions), how much weight the social score of tagging actions should have in the result build-up, or the criteria by which the user network should be explored. In order to further reduce running time, seekers are given the possibility to chose between exact or approximate answers, and can benefit from cached results of previous queries (materialized views).