document: Diversifying web search results Result diversity is a topic of great importance as more facets of queries are discovered and users expect to find their desired facets in the first page of the results . However , the underlying questions of how ` diversity ' interplays with ` quality ' and when preference should be given to one or both are not well-understood . In this work , we model the problem as expectation maximization and study the challenges of estimating the model parameters and reaching an equilibrium . One model parameter , for example , is correlations between pages which we estimate using textual contents of pages and click data ( when available ) . We conduct experiments on diversifying randomly selected queries from a query log and the queries chosen from the disambiguation topics of Wikipedia . Our algorithm improves upon Google in terms of the diversity of random queries , retrieving 14 % to 38 % more aspects of queries in top 5 , while maintaining a precision very close to Google . On a more selective set of queries that are expected to benefit from diversification , our algorithm improves upon Google in terms of precision and diversity of the results , and significantly outperforms another baseline system for result diversification .
doc_bio_tags: O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O