Abstract
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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Shen, J., Brdiczka, O., Ducheneaut, N., Yee, N., Begole, B. (2012). Inferring Personality of Online Gamers by Fusing Multiple-View Predictions. In: Masthoff, J., Mobasher, B., Desmarais, M.C., Nkambou, R. (eds) User Modeling, Adaptation, and Personalization. UMAP 2012. Lecture Notes in Computer Science, vol 7379. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31454-4_22
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DOI: https://doi.org/10.1007/978-3-642-31454-4_22
Publisher Name: Springer, Berlin, Heidelberg
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