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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