A vector space model for automatic indexing
Communications of the ACM
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Sampling search-engine results
WWW '05 Proceedings of the 14th international conference on World Wide Web
Less is more: probabilistic models for retrieving fewer relevant documents
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Addressing diverse user preferences in SQL-query-result navigation
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
New Stochastic Algorithms for Scheduling Ads in Sponsored Search
LA-WEB '07 Proceedings of the 2007 Latin American Web Conference
Introduction to Information Retrieval
Introduction to Information Retrieval
Efficient network aware search in collaborative tagging sites
Proceedings of the VLDB Endowment
Proceedings of the Second ACM International Conference on Web Search and Data Mining
It takes variety to make a world: diversification in recommender systems
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
An axiomatic approach for result diversification
Proceedings of the 18th international conference on World wide web
Efficient Computation of Diverse Query Results
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Turning down the noise in the blogosphere
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient diversity-aware search
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Collaborative personalized top-k processing
ACM Transactions on Database Systems (TODS)
P2Prec: a social-based P2P recommendation system
Proceedings of the 20th ACM international conference on Information and knowledge management
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We investigate profile diversity, a novel idea in searching scientic documents. Combining keyword relevance with popularity in a scoring function has been the subject of different forms of social relevance [2, 6, 9]. Content diversity has been thoroughly studied in search and advertising [4, 11], database queries [16, 5, 8], and recommendations [17, 10, 18]. We believe our work is the first to investigate profile diversity to address the problem of returning highly popular but too-focused documents. We show how to adapt Fagin's threshold-based algorithms to return the most relevant and most popular documents that satisfy content and profile diversities and run preliminary experiments on two benchmarks to validate our scoring function.