Integration of Heterogeneous Models with Knowledge Consolidation

For better predictions and classifications in customer recommendation, this study proposes an integrative model that efficiently combines the currently-in-use statistical and artificial intelligence models. In particular, by integrating the models such as association rule, frequency matrix, and rule induction, this study suggests an integrative prediction model. The data set for the tests is collected from a convenience store G, which is the number one in its brand in S. Korea. This data set contains sales information on customer transactions from September 1, 2005 to December 7, 2005. About 1,000 transactions are selected for a specific item. Using this data set, it suggests an integrated model predicting whether a customer buys or not buys a specific product for target marketing strategy. The performance of integrated model is compared with that of other models. The results from the experiments show that the performance of integrated model is superior to that of all other models such as association rule, frequency matrix, and rule induction.

[1]  Andreas Charitou,et al.  ANNALS OF OPERATIONS RESEARCH , 2000 .

[2]  Jang-Sun Hwang,et al.  Measures of Perceived Interactivity: An Exploration of the Role of Direction of Communication, User Control, and Time in Shaping Perceptions of Interactivity , 2002 .

[3]  Young-Gul Kim,et al.  Extending the TAM for a World-Wide-Web context , 2000, Inf. Manag..

[4]  Matthew Lombard,et al.  At the Heart of It All: The Concept of Presence , 2006 .

[5]  Michael T. Dugan,et al.  The Limitations of Bankruptcy Prediction Models: Some Cautions for the Researcher , 2001 .

[6]  M. Peel,et al.  Predicting corporate failure— Some results for the UK corporate sector , 1986 .

[7]  M. Zmijewski.  METHODOLOGICAL ISSUES RELATED TO THE ESTIMATION OF FINANCIAL DISTRESS PREDICTION MODELS , 1984 .

[8]  Jonathan Steuer,et al.  Defining virtual reality: dimensions determining telepresence , 1992 .

[9]  Amir F. Atiya,et al.  Bankruptcy prediction for credit risk using neural networks: A survey and new results , 2001, IEEE Trans. Neural Networks.

[10]  Thomas W. Malone,et al.  Toward a Theory of Intrinsically Motivating Instruction , 1981, Cogn. Sci..

[11]  Hee-Seok Song,et al.  A Methodology for Detecting the Change of Customer Behavior based on Association Rule Mining , 2001, PACIS.

[12]  David West,et al.  Neural network credit scoring models , 2000, Comput. Oper. Res..

[13]  Jonathan Crook,et al.  Credit Scoring Models in the Credit Union Environment Using Neural Networks and Genetic Algorithms , 1997 .

[14]  Steve Muylle,et al.  The role of pleasure in web site success , 2006, Inf. Manag..

[15]  Fred D. Davis.  Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[16]  Stephanie M. Bryant,et al.  A case-based reasoning approach to bankruptcy prediction modeling , 1997, Intell. Syst. Account. Finance Manag..

[17]  Michael J. A. Berry,et al.  Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management , 2004 .

[18]  F. Ricci,et al.  Personalized Product Recommendation through Interactive Query Management and Case-Based Reasoning , 2003 .

[19]  James A. Ohlson.  FINANCIAL RATIOS AND THE PROBABILISTIC PREDICTION OF BANKRUPTCY , 1980 .

[20]  Ritu Agarwal,et al.  A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology , 1998, Inf. Syst. Res..

[21]  Murugan Anandarajan,et al.  Bankruptcy prediction of financially stressed firms: an examination of the predictive accuracy of artificial neural networks , 2001, Intell. Syst. Account. Finance Manag..

[22]  Matthew Lombard,et al.  Interactive Advertising and Presence , 2001 .

[23]  Robin Burke,et al.  Knowledge-based recommender systems , 2000 .

[24]  Izak Benbasat,et al.  Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation , 1991, Inf. Syst. Res..

[25]  O. Mangasarian.  Linear and Nonlinear Separation of Patterns by Linear Programming , 1965 .

[26]  Joseph J. Martocchio,et al.  Microcomputer playfulness: development of a measure with workplace implications , 1992 .

[27]  Mun Y. Yi,et al.  Understanding information technology acceptance by individual professionals: Toward an integrative view , 2006, Inf. Manag..

[28]  Michael Y. Hu,et al.  Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis , 1999, Eur. J. Oper. Res..

[29]  Robin D. Burke,et al.  The Wasabi Personal Shopper: A Case-Based Recommender System , 1999, AAAI/IAAI.

[30]  J. Wiginton.  A Note on the Comparison of Logit and Discriminant Models of Consumer Credit Behavior , 1980, Journal of Financial and Quantitative Analysis.

[31]  John Mingers,et al.  Neural Networks, Decision Tree Induction and Discriminant Analysis: an Empirical Comparison , 1994 .

[32]  C. Heeter.  Interactivity in the Context of Designed Experiences , 2000 .

[33]  Chaochang Chiu,et al.  A case-based customer classification approach for direct marketing , 2002, Expert Syst. Appl..

[34]  Jason Bennett Thatcher,et al.  An Empirical Examination of Individual Traits as Antecedents to Computer Anxiety and Computer Self-Efficacy , 2002, MIS Q..

[35]  Viswanath Venkatesh,et al.  Creation of Favorable User Perceptions: Exploring the Role of Intrinsic Motivation , 1999, MIS Q..

[36]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[37]  Ramesh Sharda,et al.  Bankruptcy prediction using neural networks , 1994, Decis. Support Syst..

[38]  J. Efrim Boritz,et al.  Effectiveness of neural network types for prediction of business failure , 1995 .

[39]  Fred D. Davis,et al.  Extrinsic and Intrinsic Motivation to Use Computers in the Workplace1 , 1992 .

[40]  Sholom M. Weiss,et al.  Computer Systems That Learn , 1990 .

[41]  Hemant K. Bhargava,et al.  Beyond Spreadsheets: Tools for Building Decision Support Systems , 1999, Computer.

[42]  Parag C. Pendharkar,et al.  A threshold-varying artificial neural network approach for classification and its application to bankruptcy prediction problem , 2005, Comput. Oper. Res..

[43]  Melody Y. Kiang,et al.  Managerial Applications of Neural Networks: The Case of Bank Failure Predictions , 1992 .

[44]  Anurag Agarwal,et al.  Predicting the outcome following bankruptcy filing: a three-state classification using neural networks , 1997, Intell. Syst. Account. Finance Manag..

[45]  Geert Wets,et al.  Using association rules for product assortment decisions: a case study , 1999, KDD '99.

[46]  Gordon S. Linoff,et al.  Data mining techniques for marketing, sales and customer relationship , 2004 .

[47]  Mu-Chen Chen,et al.  Credit scoring and rejected instances reassigning through evolutionary computation techniques , 2003, Expert Syst. Appl..

[48]  Edward I. Altman,et al.  Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience) , 1994 .

[49]  A. K. Pujari,et al.  Data Mining Techniques , 2006 .

[50]  이건창,et al.  통계적 모형과 인공지능 모형을 결합한 기업신용평가 모형에 관한 연구 ( A Study on the Credit Evaluation Model Integrating Statistical Model and Artificial Intelligence Model ) , 1996 .

[51]  Pattie Maes,et al.  Social information filtering: algorithms for automating “word of mouth” , 1995, CHI '95.

[52]  Michael J. Pazzani,et al.  A hybrid user model for news story classification , 1999 .

[53]  John Riedl,et al.  E-Commerce Recommendation Applications , 2004, Data Mining and Knowledge Discovery.