Predicting the size of candidate document set for implicit web search result diversification

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10.1007:978-3-030-45442-5_51.pdf

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2020-01-01

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Ulu, Yasar Baris
Altıngövde, İsmail Sengör

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© Springer Nature Switzerland AG 2020.Implicit result diversification methods exploit the content of the documents in the candidate set, i.e., the initial retrieval results of a query, to obtain a relevant and diverse ranking. As our first contribution, we explore whether recently introduced word embeddings can be exploited for representing documents to improve diversification, and show a positive result. As a second improvement, we propose to automatically predict the size of candidate set on per query basis. Experimental evaluations using our BM25 runs as well as the best-performing ad hoc runs submitted to TREC (2009–2012) show that our approach improves the performance of implicit diversification up to 5.4% wrt. initial ranking.

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https://hdl.handle.net/11511/58018

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https://doi.org/10.1007/978-3-030-45442-5_51

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Department of Computer Engineering, Conference / Seminar

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Citation Formats

Y. B. Ulu and İ. S. Altıngövde, “Predicting the size of candidate document set for implicit web search result diversification,” 2020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/58018.