E-Commerce and Intelligent MethodsJavier Segovia, Piotr S. Szczepaniak, Marian Niedzwiedzinski This book covers significant recent developments in the field of Intelligent Meth ods applied to eCommerce. The Intelligent Methods considered are mainly Soft Computing Methods that include fuzzy sets, rough sets, neural networks, evolutionary computations, probabilistic and evidential reasoning, multivalued logic, and related fields. There is not doubt about the relevance of eCommerce in our daily environ ments and in the work carried out at many research centers throughout the world. The application of AI to Commerce is growing as fast as the computers and net works are being integrated in all business and commerce aspects. We felt that it was time to sit down and see how was the impact into that field of low-level AI, i.e. softcomputing. We found many scattered contributions disseminated in con ferences, workshops, journal, books or even technical reports, but nothing like a common framework that could serve as a basis for further research, comparison or even prototyping for a direct transfer to the industry. We felt then the need to set up a reference point, a book like this. We planned this book as a recompilation of the newest developments of re searchers who already made some contribution into the field. The authors were se lected based on the originality and quality of their work and its relevance to the field. Authors came from prestigious universities and research centers with differ ent backgrounds. |
Contents
| 3 | |
Some Legal Aspects of Electronic Commerce | 19 |
Competitive Neural Networks for Customer Choice Models | 41 |
a Clients Life Cycle Model Based on a Neural | 61 |
O Marbán E Menasalvas C Montes J G Rajakulendran and J Segovia | 78 |
Characterizing and Segmenting the Online Customer Market Using | 101 |
Data Mining for Diverse ECommerce Applications | 120 |
Extreme Sample Classification and Credit Card Fraud Detection | 136 |
Data Mining in Marketing Using Bayesian Networks and Evolutionary | 198 |
Improving User Profiles for ECommerce by Genetic Algorithms | 215 |
Automatic Web User Profiling and Personalization Using Robust | 233 |
Fuzzy Quantifiable Trust in Secure ECommerce | 262 |
Fuzzy Similarity in ECommerce Domains | 281 |
Intelligent Customer Support for Product Selection with CaseBased | 322 |
Mobile Agent Based Auctionlike Negotiation in Internet Retail Commerce | 342 |
| 362 | |
A Review of Evolutionary Algorithms for ECommerce | 159 |
Artificial Adaptive Market Traders Based in Genetic Algorithms for a Stock | 180 |
Other editions - View all
E-Commerce and Intelligent Methods Javier Segovia,Piotr S. Szczepaniak,Marian Niedzwiedzinski Limited preview - 2002 |
E-Commerce and Intelligent Methods Javier Segovia,Piotr S. Szczepaniak,Marian Niedzwiedzinski No preview available - 2010 |
E-Commerce and Intelligent Methods Javier Segovia,Piotr S. Szczepaniak,Marian Niedzwiedzinski No preview available - 2014 |
Common terms and phrases
analysis application approach artificial Artificial Intelligence auction automated Bayesian network behavior Case-Based Reasoning classifier cluster collaborative filtering computed consumer crossover customer’s data mining data set database defined domain e-business e-commerce electronic commerce Electronic Data Interchange evaluation evolutionary Evolutionary Algorithms evolutionary programming example experiments fitness function Fuzzy ART fuzzy set genetic algorithms genetic operators IEEE individual input intelligent interaction International Internet learning logistic logistic regression marketing matrix medoids merchants methods mutation negotiation neural networks NLDA node operators optimal output parameters patterns performance prediction problem procedure protocol purchase query relationship represents requirements retailers retrieval robust sample segmentation selection server similarity solution strategies techniques tion trade agent trust updated user profiles user sessions variables vector vendor weights Yoda