Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval
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The anatomy of a large-scale hypertextual Web search engine
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IR evaluation methods for retrieving highly relevant documents
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Item-based collaborative filtering recommendation algorithms
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Evaluating collaborative filtering recommender systems
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Questioning query expansion: an examination of behaviour and parameters
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Efficient and self-tuning incremental query expansion for top-k query processing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Usage patterns of collaborative tagging systems
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Using annotations in enterprise search
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Pruned query evaluation using pre-computed impacts
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Structure and evolution of online social networks
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IO-Top-k: index-access optimized top-k query processing
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
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Expertise networks in online communities: structure and algorithms
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Google news personalization: scalable online collaborative filtering
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Optimizing web search using social annotations
Proceedings of the 16th international conference on World Wide Web
Analysis of topological characteristics of huge online social networking services
Proceedings of the 16th international conference on World Wide Web
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Extracting semantic relations from query logs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for community identification in dynamic social networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Using social annotations to improve language model for information retrieval
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Can social bookmarking improve web search?
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
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The adaptive web
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Peer-Sensitive objectrank – valuing contextual information in social networks
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Making SENSE: socially enhanced search and exploration
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Managing collaborative feedback information for distributed retrieval
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Ontology-Based Service Discovery Front-End Interface for GloServ
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Information Processing and Management: an International Journal
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Evidence of quality of textual features on the web 2.0
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Proceedings of the 18th ACM conference on Information and knowledge management
Who tags the tags?: a framework for bookmark weighting
Proceedings of the 18th ACM conference on Information and knowledge management
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Online communities have become popular for publishing and searching content, as well as for finding and connecting to other users. User-generated content includes, for example, personal blogs, bookmarks, and digital photos. These items can be annotated and rated by different users, and these social tags and derived user-specific scores can be leveraged for searching relevant content and discovering subjectively interesting items. Moreover, the relationships among users can also be taken into consideration for ranking search results, the intuition being that you trust the recommendations of your close friends more than those of your casual acquaintances. Queries for tag or keyword combinations that compute and rank the top-k results thus face a large variety of options that complicate the query processing and pose efficiency challenges. This paper addresses these issues by developing an incremental top-k algorithm with two-dimensional expansions: social expansion considers the strength of relations among users, and semantic expansion considers the relatedness of different tags. It presents a new algorithm, based on principles of threshold algorithms, by folding friends and related tags into the search space in an incremental on-demand manner. The excellent performance of the method is demonstrated by an experimental evaluation on three real-world datasets, crawled from deli.cio.us, Flickr, and LibraryThing.