Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using association rules for product assortment decisions: a case study
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
The Wasabi Personal Shopper: a case-based recommender system
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Neural network credit scoring models
Computers and Operations Research - Neural networks in business
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural network ensemble strategies for financial decision applications
Computers and Operations Research
Computers and Operations Research
Using neural network ensembles for bankruptcy prediction and credit scoring
Expert Systems with Applications: An International Journal
Bankruptcy prediction for credit risk using neural networks: A survey and new results
IEEE Transactions on Neural Networks
An Intelligent RFID System for Consumer Businesses
GREENCOM-CPSCOM '10 Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing
Consumer behavior fuzziness in the new market environments
NNECFSIC'12 Proceedings of the 12th WSEAS international conference on Neural networks, fuzzy systems, evolutionary computing & automation
A model for complex tree integration tasks
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
Making use of associative classifiers in order to alleviate typical drawbacks in recommender systems
Expert Systems with Applications: An International Journal
Transactions on computational collective intelligence V
Selecting prospects for cross-selling financial products using multivariate credibility
Expert Systems with Applications: An International Journal
Classification based on specific rules and inexact coverage
Expert Systems with Applications: An International Journal
Save the best for last? The treatment of dominant predictors in financial forecasting
Expert Systems with Applications: An International Journal
Advanced Engineering Informatics
| Hi-index | 12.06 |
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 tree-based models (CHAID, CART, QUEST, C5.0), 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 1000 transactions are selected for a specific item. Using this data set, it suggests an integrated model predicting whether a customer buys or does not buy 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 tree-based models.