Difference between revisions of "A Method for Predicting Wikipedia Editors' Editing Interest: based on a Factor Graph Model"
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| − | '''A Method for Predicting Wikipedia Editors' Editing Interest: based on a Factor Graph Model''' - scientific work related to Wikipedia quality published in 2016, written by Haisu Zhang, Sheng Zhang, Zhaolin Wu, Liwei Huang and Yutao Ma. | + | '''A Method for Predicting Wikipedia Editors' Editing Interest: based on a Factor Graph Model''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Haisu Zhang]], [[Sheng Zhang]], [[Zhaolin Wu]], [[Liwei Huang]] and [[Yutao Ma]]. |
== Overview == | == Overview == | ||
| − | Recruiting or recommending appropriate potential Wikipedia editors to edit a specific Wikipedia entry or article can play an important role in improving the quality and credibility of Wikipedia. According to empirical observations based on a small-scale dataset collected from Wikipedia, this paper proposes an Interest Prediction Factor Graph IPFG model, which is characterized by editor's social properties, hyperlinks between Wikipedia entries, the categories of an entry and other important features, to predict an editor's editing interest in types of Wikipedia entries. Furthermore, the paper suggests a parameter learning algorithm based on the gradient descent algorithm and the Loopy Sum-Product algorithm for factor graphs. An experiment on a Wikipedia dataset with different frequencies of data collection shows that the average prediction accuracy F1 score of the IPFG model for data collected quarterly could be up to 0.875, which is approximately 0.49 higher than that of a collaborative filtering approach. In addition, the paper analyzes how incomplete social properties and editing bursts affect the prediction accuracy of the IPFG model. The authors' results can provide insight into effective Wikipedia article tossing and can improve the quality of special entries that belong to specific categories by means of collective collaboration. | + | Recruiting or recommending appropriate potential [[Wikipedia editors]] to edit a specific [[Wikipedia]] entry or article can play an important role in improving the quality and [[credibility]] of Wikipedia. According to empirical observations based on a small-scale dataset collected from Wikipedia, this paper proposes an Interest Prediction Factor Graph IPFG model, which is characterized by editor's social properties, hyperlinks between Wikipedia entries, the [[categories]] of an entry and other important [[features]], to predict an editor's editing interest in types of Wikipedia entries. Furthermore, the paper suggests a parameter learning algorithm based on the gradient descent algorithm and the Loopy Sum-Product algorithm for factor graphs. An experiment on a Wikipedia dataset with different frequencies of data collection shows that the average prediction accuracy F1 score of the IPFG model for data collected quarterly could be up to 0.875, which is approximately 0.49 higher than that of a collaborative filtering approach. In addition, the paper analyzes how incomplete social properties and editing bursts affect the prediction accuracy of the IPFG model. The authors' results can provide insight into effective Wikipedia article tossing and can improve the quality of special entries that belong to specific categories by means of collective collaboration. |
Revision as of 07:14, 29 August 2019
A Method for Predicting Wikipedia Editors' Editing Interest: based on a Factor Graph Model - scientific work related to Wikipedia quality published in 2016, written by Haisu Zhang, Sheng Zhang, Zhaolin Wu, Liwei Huang and Yutao Ma.
Overview
Recruiting or recommending appropriate potential Wikipedia editors to edit a specific Wikipedia entry or article can play an important role in improving the quality and credibility of Wikipedia. According to empirical observations based on a small-scale dataset collected from Wikipedia, this paper proposes an Interest Prediction Factor Graph IPFG model, which is characterized by editor's social properties, hyperlinks between Wikipedia entries, the categories of an entry and other important features, to predict an editor's editing interest in types of Wikipedia entries. Furthermore, the paper suggests a parameter learning algorithm based on the gradient descent algorithm and the Loopy Sum-Product algorithm for factor graphs. An experiment on a Wikipedia dataset with different frequencies of data collection shows that the average prediction accuracy F1 score of the IPFG model for data collected quarterly could be up to 0.875, which is approximately 0.49 higher than that of a collaborative filtering approach. In addition, the paper analyzes how incomplete social properties and editing bursts affect the prediction accuracy of the IPFG model. The authors' results can provide insight into effective Wikipedia article tossing and can improve the quality of special entries that belong to specific categories by means of collective collaboration.
