Document clustering with universum
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
A query performance analysis for result diversification
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
Combining implicit and explicit topic representations for result diversification
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Reranking web search results for diversity
Information Retrieval
Measuring the coverage and redundancy of information search services on e-commerce platforms
Electronic Commerce Research and Applications
Ranking document clusters using markov random fields
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
A Diagnostic Study of Search Result Diversification Methods
Proceedings of the 2013 Conference on the Theory of Information Retrieval
Why not, WINE?: towards answering why-not questions in social image search
Proceedings of the 21st ACM international conference on Multimedia
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Result diversification is a retrieval strategy for dealing with ambiguous or multi-faceted queries by providing documents that cover as many facets of the query as possible. We propose a result diversification framework based on query-specific clustering and cluster ranking, in which diversification is restricted to documents belonging to clusters that potentially contain a high percentage of relevant documents. Empirical results show that the proposed framework improves the performance of several existing diversification methods. The framework also gives rise to a simple yet effective cluster-based approach to result diversification that selects documents from different clusters to be included in a ranked list in a round robin fashion. We describe a set of experiments aimed at thoroughly analyzing the behavior of the two main components of the proposed diversification framework, ranking and selecting clusters for diversification. Both components have a crucial impact on the overall performance of our framework, but ranking clusters plays a more important role than selecting clusters. We also examine properties that clusters should have in order for our diversification framework to be effective. Most relevant documents should be contained in a small number of high-quality clusters, while there should be no dominantly large clusters. Also, documents from these high-quality clusters should have a diverse content. These properties are strongly correlated with the overall performance of the proposed diversification framework. © 2011 Wiley Periodicals, Inc.