An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
ACM Computing Surveys (CSUR)
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Exploring social annotations for the semantic web
Proceedings of the 15th international conference on World Wide Web
tagging, communities, vocabulary, evolution
CSCW '06 Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Applying Cross-Level Association Rule Mining to Cold-Start Recommendations
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
Can social bookmarking improve web search?
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Addressing cold-start problem in recommendation systems
Proceedings of the 2nd international conference on Ubiquitous information management and communication
Tag-aware recommender systems by fusion of collaborative filtering algorithms
Proceedings of the 2008 ACM symposium on Applied computing
Social ranking: uncovering relevant content using tag-based recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
The wisdom of the few: a collaborative filtering approach based on expert opinions from the web
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Collaborative tagging in recommender systems
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Mutual contextualization in tripartite graphs of folksonomies
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Incremental collaborative filtering for highly-scalable recommendation algorithms
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems
Expert Systems with Applications: An International Journal
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Folksonomies have become a powerful tool to describe, discover, search, and navigate online resources (e.g., pictures, videos, blogs) on the Social Web. Unlike taxonomies and ontologies, which overimpose a hierarchical categorisation of content, folksonomies empower end users, by enabling them to freely create and choose the categories (in this case, tags) that best describe a piece of information. However, the freedom afforded to users comes at a cost: as tags are informally defined and ungoverned, the retrieval of information becomes more challenging. In this paper, we propose Clustered Social Ranking (CSR), a novel search and recommendation technique specifically developed to support new users of Web 2.0 websites finding content of interest. The observation underpinning CSR is that the vast majority of content on Web 2.0 websites is created by a small proportion of users (leaders), while the others (followers) mainly browse such content. CSR first identifies who the leaders are; it then clusters them into communities with shared interests, based on their tagging activity. Users' queries (be them searches or recommendations) are then directed to the community of leaders who can best answer them. Our evaluation, conducted on the CiteULike dataset, demonstrates that CSR achieves an accuracy that is comparable to the best state-of-the-art techniques, but at a much smaller computational cost, thus affording it better scalability in these fast growing settings.