Wikipedia edit number prediction based on temporal dynamics only

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Wikipedia edit number prediction based on temporal dynamics only
Authors: Dell Zhang
Citation: missing journal missing volume : missing pages. 2011 October
Database(s): arXiv (Arxiv/1110.5051)
DOI: Define doi.
Link(s): http://arxiv.org/pdf/1110.5051.pdf
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Wikipedia edit number prediction based on temporal dynamics only describes a machine learning system for prediction of Wikipedia edits.

The system came in third in the Wikipedia Participation Challenge.

[edit] Data

It was an entry in the Wikipedia Participation Challenge and used data from this competition.

[edit] Method

They train a supervized learning model with data from the edit time series with the target also extracted from the edit time series, - what they called "self-supervized learning". They use no other feature than the edit history.

They use a "log(1+a)" link function and a square loss function.

They use Python scikit-learn and OpenCV and tried OLS, SVM, KNN, ANN, GBT with default parameters Gradient Boosted Tree outperformed the other methods and they then explored (hyper)parameters of the GBT model.

[edit] Related papers

  1. Identifying important factors for future contribution of Wikipedia editors
  2. Wikipedia edit number prediction from the past edit record based on auto-supervised learning
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