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Information Gain Clustering through Prototype-Embedded Genetic K-Mean Algorithm (IGCPGKA): a novel heuristic approach for personalisation of cold start problem

Published: 26 October 2012 Publication History

Abstract

Information Gain Clustering through Prototype - Embedded Genetic K-Mean Algorithm (IGCPGKA) is a novel heuristic used in Recommendation System (RS) for solving personalization problems.
In a bid to generate information on the behavior and effectiveness of Prototype-Embedded Genetic K-mean Algorithm (PGKA) -- a clustering algorithm - in Recommender System (RS) used in personalization of cold start problem, IGCPGKA is proposed, developed and experimented upon in this work/paper.
IGCPGKA is derived from IGCEGA (Information Gain Clustering through Elitizt Genetic Algorithm). The main difference between the two algorithms is articulated and exhibited in the clustering stage, and precisely, PGKA is used in IGCPGKA, while EGA (Elitizt Genetic Algorithm) is used in IGCEGA for clustering purposes. The effects of these differences have created positive results in terms of better recommendation for IGCPGKA and this fact is supported by the two evaluation metrics used in this work, namely Expected Utility (EU) and Mean Absolute Error (MAE).
A comparison with other heuristics for personalization of cold start problem - such as Information Gain Clustering Neighbor through Bisecting K-Mean Algorithm (IGCN), Information Gain Clustering through Genetic Algorithm (IGCGA), entropy and popularity -- showed that IGCPGKA emerged vector by producing the best recommendation and this fact is also supported by the two evaluation metrics used in this work.

References

[1]
Mohd Abdul Hameed, OmarAl Jadaan, and S. Ramachandaram, "Information theorectic aproach to cold start problem using Genetic Algorithm", IEEE, 2010, ISBN: 978-0-7695-4254-6.
[2]
Al Mamunur Rashid, Gerge Karypis and John Riedl, "Learning Preferences of new users in Recommender System: An Information Approach.", SIGKDD Workshop on Web Mining and Web Usage Analysis (WEBKDD), 2008.
[3]
Al Mamunur Rashid, Istvan albert, Dan Cosley, Shyong K. Lam, Sean MCNee, Joseph A. Konstan, and John Riedl, "Learn New User Preferences in Recomender Systems, 2002 international Conference on Intelliganent User interfaces. pp. 127--134.
[4]
Isabelle Guyon, Nada Matic, and Vladimir Vapnik, "Discovering Informative Patterns and data cleaning", 1996.
[5]
Mitchell Thomas M., "Machine Learning", McGraw-Hill Higher Education, 1997.
[6]
Omar Al Jadaan, Lakshmi Rajamani, and C. R. Rao, "Improved selection operator for Genetic Algorithm", Journal of Theoretical and Applied Information Technology, Vol. 4, No. 4, pp. 269--277, 2008.
[7]
Daniel Billsus and Michael J. Pazzani, "Learning collaborative information filters", Proc. 15th International Conference on Machine Learning, pages web personalization problem 46--54. Morgan Aufmann, San Francisco, CA, 1998.
[8]
K. Krishna and M. Narasimha Murty, "Genetic K-means algorithm," IEEE Trans. Systems, Man, and Cybernetics, 29(3), June 1999.
[9]
Y. Lu, S. Lu, F. Fotouhi, Y. Deng, and S. J. Brown, "FGKA: A fast genetic K-means clustering algorithm," Proc. ACM Symposium on Applied Computing, 2004.
[10]
Shih-Sian Cheng1,2, Yi-Hsiang Chao1,2, Hsin-Min Wang2, and Hsin-Chia Fu1, "A Prototypes-Embedded Genetic K-means Algorithm" The 18th International Conference on Pattern Recognition (ICPR'06) 0-7695-2521-0/06, 2006 IEEE
[11]
A. E. Eiben and J. E. Smith, Introduction to Evolutionary Computing, Springer, Berlin, 2003.
[12]
Mohd Abdul Hameed, S. Ramachandram, Omar Al Jadaan, "IGCEGA: A Novel Heuristic Approach For Personalisation Of Cold Start Problem", 2011 International Conference on Communication Systems and Network Technologies, Jammu, Katra, India: IEEE 2011.

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    cover image ACM Other conferences
    CCSEIT '12: Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
    October 2012
    800 pages
    ISBN:9781450313100
    DOI:10.1145/2393216
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 26 October 2012

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    Author Tags

    1. Expected Utility (EU)
    2. K-mean operator (KMO) and genetic k-mean algorithm (GKA)
    3. Mean Absolute Error (MAE)
    4. Recommendation System (RS)
    5. entropy
    6. personalization
    7. popularity
    8. web personalization

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    • (2023)NDeep Learning Heart Stroke Prediction Model Integration of MMAM with NB(MMAM-NB) and DT(MMAM-DT)2023 IEEE 5th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA)10.1109/ICCCMLA58983.2023.10346833(373-380)Online publication date: 7-Oct-2023
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