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Decision Making Process for B2C Model Using Behavior Analysis with Big Data Technologies
Objectives: Business to Consumer (B2C) E-Commerce activities are developed with a large number through agent-based systems. Case Based Reasoning (CBR) has been applied in these systems by analyzing the consumer buying behavior to provide consumers, a support to the decision making process. Analysis: Current applications of CBR to E-Commerce are limited to fixed, unchangeable products. To make the environment support for configurable products, an interactive operator based customization approach from CBR can be applied. Findings: In this work, to make the process more reliable and efficient, real time data from provisional stores has been taken and the system is trained to predict the consumer buying behavior along with CBR to pave way for a consumer to make a better decision making process. Applications/ Improvements: This work also applies big data concepts in predicting the behavior of the consumers. It thereby also led the customers to mine about their preferences in purchasing necessary products.
Big Data, Business to Consumer E-Commerce Activities, Case based Reasoning, Multi Agent Systems, Semantic Web.
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