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We note that the number of the proposed queries is equal to five queries whatever is the value of k (the number of queries).
The precision value remains almost unchanged when k is greater than 3, this is explained by the idea that the number of the proposed queries is equal to five queries whatever is the value of k fixed.
For the following reasons: We believe that when the number of recommendations exceeds five queries, the user will be tempted to read, analyze and compare the proposed queries to make his choice.
Indeed, at least 40%% of the proposed queries were triggered by users to progress in their analysis process, this rate is generally higher than 70%% (when k exceeds 2 queries) and reaches 76 % when the number of candidate queries is optimal.
A few examples of the proposed queries are given below.
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Moreover, the overall outcome of the evaluation dictates that the proposed query construction service moves on the right tracks.
The proposed query construction service acts as an intermediate layer between searchers and large-scale web search engines.
Next, we present the GUI of the proposed query construction service and illustrate through several examples how it contributes to the overall search process on the web.
Next, we present the middleware of the proposed query construction service and illustrate how it contributes to the overall search process.
The consecutive interactions of a web searcher with the proposed query construction service in the context of satisfying a specific information need is considered as a search session.
Experimental results on the datasets of TREC web track, OHSUMED, and a commercial web search engine show that with the use of the proposed query-level loss function we can significantly improve ranking accuracies.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com