Difference between revisions of "Readers' Demanded Hyperlink Prediction in Wikipedia"
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| − | '''Readers' Demanded Hyperlink Prediction in Wikipedia''' - scientific work related to Wikipedia quality published in 2018, written by Laxmi Amulya Gundala and Francesca Spezzano. | + | '''Readers' Demanded Hyperlink Prediction in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Laxmi Amulya Gundala]] and [[Francesca Spezzano]]. |
== Overview == | == Overview == | ||
| − | In this paper, authors describe on-going research on the problem of predicting needed hyperlinks between pairs of Wikipedia pages (u,v) that are not connected, yet show readers' search navigation from u to v. Authors propose a solution that first estimates how long will these searches last and then predicts new hyperlinks according to descending order of duration. Authors initial experimental results show that best solution achieves an AUROC of 0.77 on the Wikipedia Clickstream dataset and a [email protected]% of 1.0 and significantly beats the baselines. | + | In this paper, authors describe on-going research on the problem of predicting needed hyperlinks between pairs of [[Wikipedia]] pages (u,v) that are not connected, yet show readers' search navigation from u to v. Authors propose a solution that first estimates how long will these searches last and then predicts new hyperlinks according to descending order of duration. Authors initial experimental results show that best solution achieves an AUROC of 0.77 on the Wikipedia Clickstream dataset and a [email protected]% of 1.0 and significantly beats the baselines. |
Revision as of 00:27, 27 September 2019
Readers' Demanded Hyperlink Prediction in Wikipedia - scientific work related to Wikipedia quality published in 2018, written by Laxmi Amulya Gundala and Francesca Spezzano.
Overview
In this paper, authors describe on-going research on the problem of predicting needed hyperlinks between pairs of Wikipedia pages (u,v) that are not connected, yet show readers' search navigation from u to v. Authors propose a solution that first estimates how long will these searches last and then predicts new hyperlinks according to descending order of duration. Authors initial experimental results show that best solution achieves an AUROC of 0.77 on the Wikipedia Clickstream dataset and a [email protected]% of 1.0 and significantly beats the baselines.
