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http://dx.doi.org/10.7236/JIWIT.2012.12.4.95

An Implementation of Story Path Recommendation System of Interactive Drama Using PCA and NMF  

Lee, Yeon-Chang (Dept. of Medical IT & Marketing, Eulji University)
Jang, Jae-Hee (Dept. of Medical IT & Marketing, Eulji University)
Kim, Myung-Gwan (Dept. of Medical IT & Marketing, Eulji University)
Publication Information
The Journal of the Institute of Internet, Broadcasting and Communication / v.12, no.4, 2012 , pp. 95-102 More about this Journal
Abstract
Interactive drama is a story which requires user's free choice and participation. In this study, we grasp user's preference by making training data that utilize characters of interactive drama. Furthermore, we describe process of implementing systems which recommend new users path of stories that correspond with their preference. We used PCA and NMF to extract characteristic of preference. The success rate of recommending was 75% with PCA, while 62.5% with NMF.
Keywords
Interactive Drama; PCA(Principal Component Analysis); NMF(Non-negative Matrix Factorize); Feature Extraction; Data Mining;
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  • Reference
1 Yoon H J, A study on story generation model of interactive drama, Journal of Korean Society for Computer Game , vol. 3, NO 21, 2010 June.
2 Hong Yu and Mark O. Riedl, A Sequential Recommendation Approach for Interactive Personalized Story Generation, Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems, Valencia, Spain, 2012 June.
3 David K. Elson and Mark O. Riedl, A Lightweight Intelligent Virtual Cinematography System for Machinima Production, Association for the Advancement of Artificial Intelligence, 2007.
4 Erkki Oja, Principal components, minor components, and linear neural networks, Neural Networks, vol. 5, pp. 927-935, 1992.   DOI
5 Daniel D. Lee and H. Sebastian Seung, Learning the parts of objects by non-negative matrix factorization, Nature, vol. 401, pp. 788-791, 1999.   DOI   ScienceOn
6 Daniel D. Lee and H. Sebastian Seung, Algorithms for Non-negative Matrix Factorization, In Advances in Neural Infromation Processing System, vol. 13, pp. 556-562, 2001.
7 D. K. Lee, J. H. Kwon, "Social Search Algorithm considering Recent Interests of User", Journal of Korean Institute of Information Technology, vol. 9, issue 4, pp. 187-194, Apr 2011
8 Brenda Laurel, Reassessing Interactivity, The Journal of Computer Game Design, vol. 1, No. 3, 1987 October-November.
9 Brenda Laurel, Computer as Theatre, Addison Wesely Longman, 1993.
10 Janet Murray, Interactive Storytelling, An Graphics, pp. 310, 2001.
11 Michael Mateas, Interactive Drama, Art and Artificial Intelligence, School of Computer Science Carnegie Mellon University, pp. 3, 2002.
12 Shailesh Kumar, Joydeep Ghosh, and Melba M, Best-Bases Feature Extraction Algorithms for Classification of Hyperspectral Data, IEEE Transactions on geoscience and remote sensing, Vol. 39, No. 7, 2001 July.
13 Michael E. Tipping and Christopher M. Bishop, Probabilistic Principal Component Analysis, Journal of the Rcyal Statistical Society, Vol. 61, No.3, pp 611-622, 1999.   DOI
14 Henning Risvik, Principal Component Analysis (PCA) & NIPALS algorithm, 2007 May.
15 Toby Segaran, Programming Collective Intelligence: Building Smart Web 2.0 Applications, O'REILLY, pp. 300-302, 2007.