Total views : 462

Intelligent Recommender System for High Dimensional Transaction Data Set with Complex Relationships among the Variables

Affiliations

  • Department of Industrial and Management Systems Engineering, Dong-A University, Busan, Korea, Republic of
  • Department of Game Engineering, Paichai University, Daejeon, Korea, Republic of

Abstract


Background/Objectives: This paper aims to develop a novel intelligent recommender system suitable for high dimensional data where multiple factor variables influence on multiple response variables. Methods/Statistical Analysis: This paper suggests that the structure of the cause-and-effect relations among the variables can be represented in a simple form called structured association network model (SANM). Based on the SANM and conventional data mining techniques such as association rule mining and naïve Bayesian classifier, the proposed recommender system computes three novel recommendation scores for each response variables, and the variables with high scores can be selected for recommendation. Findings: For illustration, the proposed recommender system has been applied to a mass health examination result data set. Owing to its simple structure, a SANM for a given data set can be easily obtained by simply identifying the factor and the response variables, and the experiment results revealed that the proposed system can identify the recommendable items for the individuals more effectively than the traditional classification techniques such as naïve Bayesian classifier. Consequently, we can conclude that the proposed recommender system can deal with high dimensional transaction data set in more effective manner than the traditional approaches where the underlying semantic relationships among the variables are not considered. Applications/Improvements: The proposed recommender system is useful for evaluation of the potential risks of specific diseases. Moreover, the recommendation scores can also be used as a tool for feature construction.

Keywords

Association Rule Mining, Data Mining, Recommendation, Structured Association Network Model, Transaction Data.

Full Text:

 |  (PDF views: 174)

References


  • Lu J, Wu D, Mao M, Wang W, Zhang G. Recommender system application developments: a survey. Decision Support Systems. 2015 Jun; 74(C):12-32.
  • Lee S, Lee E, Park J-M. An optimal sorting algorithm for mobile devices. Indian Journal of Science and Technology. 2015 Apr; 8(8):226-32.
  • Kim KJ, Ahn H. A recommender system using GA K-means clustering in an online shopping market. Expert Systems with Applications. 2008 Feb; 34(2):1200-209.
  • Kim JW, Ha SH. Price comparisons on the internet based on computational intelligence. Plos-One. 2014 Sep; 9(9):e106946.
  • Felfernig A, Teppan E, Gula B. Knowledge-based recommender technologies for marketing and sales. International Journal of Pattern Recognition and Artificial Intelligence. 2007 Mar; 21(2):333-55.
  • Ngai EW, Xiu L, Chau DC. Application of data mining techniques in customer relationship management: a literature review and classification. Expert Systems with Applications. 2009 Mar; 36(2):2592-602.
  • Musto C, Semeraro G, Lops P, de Gemmis M, Lekkas G. Personalized finance advisory through case-based recommender systems and diversification strategies. Decision Support Systems. 2015 Sep; 77(C):100-111.
  • Hsu MH. A personalized English learning recommender system for ESL students. Expert Systems with Applications. 2008 Jan; 34(1):683-88.
  • Duan L, Street WN, Xu E. Healthcare information systems: data mining methods in the creation of a clinical recommender system. Enterprise Information Systems. 2011 Jan; 5(2):169-81.
  • Bodadilla J, Ortega F, Hernando A, Gutierrez A. Recommender systems survey. Knowledge-Based Systems. 2013 Jul; 46:109-32.
  • Vialardi C, Bravo Agapito J, Shafti LS, Ortigosa A. Recommendation in higher education using data mining techniques. Spain: Proceedings of the 2nd International Conference on Educational Data Mining. 2009 Jul; p. 1-10.
  • Bridge D, Goker MH, McGinty L, Smyth B. Case-based recommender systems. The Knowledge Engineering Review. 2005 Sep; 20(3):315-20.
  • Azaria A, Hassidim A, Kraus S, Eshkol A, Weintraub O, Netanely I. Movie recommender system for profit maximization. China: Proceedings of the 7th ACM Conference on Recommender Systems. 2013 Oct; p. 1-8.
  • Ramezani A, Dehkordi MN, Esfahani FS. Hiding sensitive association rules by elimination selective item among RHS items for each selective transaction. Indian Journal of Science and Technology. 2014 Jun; 7(6):826-32.
  • Agrawal R, Imielinski R, Swami R. Mining associations between sets of items in massive databases. USA: Proceedings of the ACM-SIGMOD 1993 International Conference on Management of Data. 1993; p. 1-10.
  • Agrawal R, Srikant R. Fast algorithms for mining association rules. Chile: Proceedings of the International Conference on Very Large Databases. 1994; p. 1-13.
  • Tan PN, Steinbach M, Kumar V. Boston: Addison-Wesley: Introduction to data mining. 1st edn. 2005.
  • Hipp J, Guntzer U, Nakhaeizadeh G. Algorithms for association rule mining - a general survey and comparison. ACM SIGKDD Explorations Newsletter. 2000 Jun; 2(1):58-64.
  • Lin W, Alvarez SA, Ruiz C. Efficient adaptive-support association rule mining for recommender systems. Data Mining and Knowledge Discovery. 2002 Jan; 6(1):83-105.
  • Garcia E, Romero C, Ventura S, de Castro C. A collaborative educational association rule mining tool. The Internet and Higher Education. 2011 Mar; 14(2):77-88.
  • Yager RR. An extension of the naive Bayesian classifier. Information Sciences. 2006 Mar; 176(5):577-88.
  • Yin Y, Yasuda K. Similarity coefficient methods applied to the cell formation problem: a comparative investigation. Computers and Industrial Engineering. 2005 May; 48(3):471-89.
  • Weka, data mining software in Java. Date accessed: 03/21/2016: Available from: http://www.cs.waikato.ac.nz/ml/weka.

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.