Predicting Wikipedia Editor´s Editing Interest Based on Factor Graph Model

Recruiting or recommending appropriate latent editors who can edit a specific entry (or called article) plays an important role in improving the quality of Wikipedia entries. To predict an editor´s editing interest for Wikipedia entries, this paper proposes an Interest Prediction Factor Graph (IPFG) model, which is characterized by editor´s social properties, hyperlinks between Wikipedia entries, categories of an entry and other important features. Furthermore, the paper suggests a parameter learning algorithm based on the gradient descent and Loopy Sum-Product algorithms for factor graphs. The experiment on a Wikipedia dataset shows that, the average prediction accuracy (F1-Measure) of the IPFG model could be up to 87.5%, which is about 35% higher than that of a collaborative filtering approach. Moreover, the paper analyses how incomplete social properties and editing bursts affect the prediction accuracy of the IPFG model. What we found would provide a useful insight into effective Wikipedia article tossing, and improve the quality of those entries that belong to specific categories by means of collective collaboration.