Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Optimal aggregation algorithms for middleware
Journal of Computer and System Sciences - Special issu on PODS 2001
Questioning query expansion: an examination of behaviour and parameters
ADC '04 Proceedings of the 15th Australasian database conference - Volume 27
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
Journal of Information Science
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
Optimizing web search using social annotations
Proceedings of the 16th international conference on World Wide Web
ACM Transactions on the Web (TWEB)
Guest Editors' Introduction: Social Media and Search
IEEE Internet Computing
Using social annotations to improve language model for information retrieval
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Efficient top-k querying over social-tagging networks
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Taagle: efficient, personalized search in collaborative tagging networks
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Design of a P2P content recommendation system using affinity networks
Computer Communications
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Online communities like Flickr, del.icio.us and YouTube have established themselves as very popular and powerful services for publishing and searching contents, but also for identifying other users who share similar interests. In these communities, data are usually annotated with carefully selected and often semantically meaningful tags, collaboratively chosen by the user who uploaded an item and other users who came across the item. Items like urls or videos are typically retrieved by issueing queries that consist of a set of tags, returning items that have been frequently annotated with these tags. However, users often prefer a more personalized way of searching over such a 'global' search, exploiting preferences of and connections between users. The SENSE system presented in this demo supports hybrid personalization along two dimensions: in the social dimension, a search process is focused towards items tagged by users explicitly selected as friends by the querying user, whereas in the spiritual dimension, users that share preferences with the querying user are preferred. Orthorgonal to this, the system additionally integrates semantic expansion of query tags to improve search results. SENSE provides an efficient top-k algorithm that dynamically expands the search to related users and tags. It is based on principles of threshold algorithms, folding related users and tags into the search space in an incremental on-demand manner, thus visiting only a small fraction of the social network when evaluating a query. The demonstration uses three different real-world datasets: a large set of urls from del.icio.us, a large set of pictures from Flickr, and a large set of books from librarything, each together with a large fraction of the corresponding social network of these sites.