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Inferring personality of online gamers by fusing multiple-view predictions

 

Reliable personality prediction can have direct impact on many adaptive systems, such as targeted advertising, interface personalization and content customization. We propose an algorithm to infer a user's personality profile more reliably by fusing analytical predictions from multiple sources including behavioral traces, textual data, and social networking information. We applied and validated our approach using a real data set obtained from 1,040 World of Warcraft players. Besides behavioral and social networking information, we found that text analysis of character names yields the strongest personality cues.

 
 
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citation

Shen, J.; Brdiczka, O.; Ducheneaut, N.; Yee, N.; Begole, J. Inferring personality of online gamers by fusing multiple-view predictions. Twentieth International Conference on User Modeling, Adaptation and Personalization (UMAP); 2012 July 16-20; Montreal, Canada. Berlin: Springer; 2012; LNCS vol. 7379: 261-273.

copyright

Copyright © Springer Verlag, 2012. All rights reserved. The original publication is available at www.springerlink.com.