Supervised learning methods for diversification of image search results

© Springer Nature Switzerland AG 2020.We adopt a supervised learning framework, namely R-LTR [17], to diversify image search results, and extend it in various ways. Our experiments show that the adopted and proposed variants are superior to two well-known baselines, with relative gains up to 11.4%

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

© 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

What Can Task Teach Us About Query Reformulations?

International audienceA significant amount of prior research has been devoted to understanding query reformulations. The majority of these works rely on time-based sessions which are sequences of contiguous queries segmented using time threshold on users’ activities. However, queries are generally issued by users having in mind a particular task, and time-based sessions unfortunately fail in revealing such tasks. In this paper, we are interested in revealing in which extent time-based sessions vs. task-based sessions represent significantly different background contexts to be used in the perspective of better understanding users’ query reformulations. Using insights from large-scale search logs, our findings clearly show that task is an additional relevant search unit that helps better understanding user’s query reformulation patterns and predicting the next user’s query. The findings from our analyses provide potential implications for model design of task-based search engines