An Artificial Neural Network Classification Approach For Improving Accuracy Of Customer Identification In E-Commerce

Malaysian Journal of Computer Science

Nader Sohrabi Safa¹  Maizatul Akmar Ismail¹  Norjihan Abdul Ghani¹ 

关键词: Customer identification;    Behavioral pattern;    Profile;    e-Commerce;   

DOI  :  

学科分类：社会科学、人文和艺术（综合）

来源: University of Malaya * Faculty of Computer Science and Information Technology

【 摘 要 】

With the advancesin Web-based oriented technologies, experts are able to capture user activities on the Web. Users�?Web browsing behavior is used for user identification. Identifying users during their activities is extremelyimportant in electronic commerce (e-Commerce)as it has the potential to prevent illegal transactions or activitiesparticularly for users who enter the system through the use of unknown methods.In addition, customer behavioralpattern identification provides a wide spectrum of applications such as personalized Web pages, productrecommendations and present advertisements. In this research, a framework for users�? behavioral profilingformation is presented and customer behavioral patternsare used for customer identification in the e-Commerceenvironment. Based on activity control, policies such as user restriction or blockingcan be applied.The neuralnetwork classification and the measure of similarity among behavioral patterns are two approaches applied in thisresearch. The results of multi-layer perceptron with a back propagation learning algorithm indicate that there isless error and up to 15.12% more accuracy on average.The results imply that the accuracy of the neural networkapproach in customer pattern behavior recognition increases when the number of customers grows.In contrast, theaccuracy of the similarity of pattern method decreases.

【 授权许可】

Unknown