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• W139504123 abstract "Aggregated search nowadays has become a widespread technique of generating Search Engine Result Page SERP. The main task of aggregated search is incorporating results from a number of specialized search collections often referenced as verticals into a ranked list of Web-search results. To proceed with the blending algorithm one can use a variety of different sources of information starting from some textual features up to query-log data. In this paper we study the usefulness of personalized features for improving the quality of aggregated search ranking. The study is carried out by training a number of machine-learned blending algorithms, which differ by the sets of used features. Thus we not only measure the value of personalized approach in the aggregated search context, but also find out which classes of personalized features outperform the others." @default.
• W139504123 created "2016-06-24" @default.
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• W139504123 date "2014-01-01" @default.
• W139504123 modified "2023-09-27" @default.
• W139504123 title "Personalizing Aggregated Search" @default.
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• W139504123 doi "https://doi.org/10.1007/978-3-319-06028-6_17" @default.
• W139504123 hasPublicationYear "2014" @default.
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