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Intelligent Recommendation Processor Simulation using Association Relationship

연관관계를 이용한 지능형 추천 프로세스 시뮬레이션

  • Han, Jung-Soo (Division of Information & Communication, Baekseok Univ.)
  • 한정수 (백석대학교 정보통신학부)
  • Received : 2013.12.01
  • Accepted : 2013.12.20
  • Published : 2013.12.28

Abstract

In this paper we proposed a intelligent recommendation processor that the type of auto parts failures that may occur in the checkout process is represented by association relationship and the relationship was implemented with ontology. For this purpose, we defined 10 kinds of failure types and their associated parts, and we designed to simulate the recommendation process of five views. For components to be checked with the type of fault, it was possible to be expansion recommendation to the intelligent by controlling the weight value according to the relationship on the components.

Keywords

Association Relationship;Recommendation Processor;Component;Ontology;Extended Retrieval

References

  1. Jung-Soo Han, Gui-Jung Kim ,A method of intelligent recommendation using task ontology, Cluster Computing, 2013. doi:10.1007/s10586-013-0288-1, https://doi.org/10.1007/s10586-013-0288-1
  2. Jong-Won Ko, Su-Jin Baek, Gui-Jung Kim, Intelligent recommendation system for automotive parts assembly, Lecture Notes in Electrical Engineering(IT Convergence and Security 2012), Vol. 2, pp.1165-1170, 2012.
  3. Kim, Y., Kim, J., Attack detection in recommender systems using a rating stream trend analysis, J. Korea Soc. Internet Inf., Vol. 12, No. 2, pp.85-101, 2011.
  4. Jonathan L. Herlocker, Joseph A. Konstan, and John Riedl. Explaining Collaborative Filtering Recommendations. CSCW''00, December, Philadelphia, PA., 2000.
  5. Nguyen, N.T., Computational collective intelligence, Semantic web.social networks and multiagent systems, In: ICWS 2007, pp. 1164-1167, 2007.
  6. Kim, G.-J., Han, J.-S., Application method of task ontology technology for recommendation of automobile parts, J. Dig. Policy Manag.. Vol. 10, No. 6, pp.275-282, 2012.
  7. Vuong Xuan Tran, OWL-T: A Task Ontology Language for Automatic Service Composition. ICCCI 2009.
  8. Huiqing, N., Hong, C., An improved recommendation algorithm in knowledge network, J. Netw., Vol. 8, No. 6, pp.1336-1342, 2013. doi:10.4304/jnw.8.6.1336-1342 https://doi.org/10.4304/jnw.8.6.1336-1342
  9. H.J. Suh, Y.H. Kim, S.W. Lee, J.S. Lee. e-learning Technology Based on Mixed Reality. Electronics and Telecommunications Trends, Vol. 24, No. 1, 2009.
  10. Kim, J.H., Chung, K.Y., Ontology-based healthcare context information model to implement ubiquitous environment, Multimed. Tools Appl., 2013. doi:10.1007/s11042-011-0919-6 https://doi.org/10.1007/s11042-011-0919-6

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