Difference between revisions of "Wikipedia Edit Number Prediction based on Temporal Dynamics Only"
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| − | '''Wikipedia Edit Number Prediction based on Temporal Dynamics Only''' - scientific work related to Wikipedia quality published in 2011, written by Dell Zhang. | + | '''Wikipedia Edit Number Prediction based on Temporal Dynamics Only''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Dell Zhang]]. |
== Overview == | == Overview == | ||
| − | In this paper, authors describe approach to the Wikipedia Participation Challenge which aims to predict the number of edits a Wikipedia editor will make in the next 5 months. The best submission from team, "zeditor", achieved 41.7% improvement over WMF's baseline predictive model and the final rank of 3rd place among 96 teams. An interesting characteristic of approach is that only temporal dynamics features (i.e., how the number of edits changes in recent periods, etc.) are used in a self-supervised learning framework, which makes it easy to be generalised to other application domains. | + | In this paper, authors describe approach to the [[Wikipedia]] Participation Challenge which aims to predict the number of edits a Wikipedia editor will make in the next 5 months. The best submission from team, "zeditor", achieved 41.7% improvement over WMF's baseline predictive model and the final rank of 3rd place among 96 teams. An interesting characteristic of approach is that only temporal dynamics [[features]] (i.e., how the number of edits changes in recent periods, etc.) are used in a self-supervised learning framework, which makes it easy to be generalised to other application domains. |
Revision as of 23:00, 28 May 2019
Wikipedia Edit Number Prediction based on Temporal Dynamics Only - scientific work related to Wikipedia quality published in 2011, written by Dell Zhang.
Overview
In this paper, authors describe approach to the Wikipedia Participation Challenge which aims to predict the number of edits a Wikipedia editor will make in the next 5 months. The best submission from team, "zeditor", achieved 41.7% improvement over WMF's baseline predictive model and the final rank of 3rd place among 96 teams. An interesting characteristic of approach is that only temporal dynamics features (i.e., how the number of edits changes in recent periods, etc.) are used in a self-supervised learning framework, which makes it easy to be generalised to other application domains.
