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A framework for improving e-commerce websites usability using a hybrid genetic algorithm and neural network system

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Abstract

An ever-increasing use of Internet has greatly been affecting the importance of e-commerce websites. As such, analyzing those websites and discovering customers’ purchasing power, the present study tends to propose a framework and an appropriate structure to make the websites more flexible and highly functional. Such an efficient and effective framework facilitates customers’ purchasing capacities. In this framework, different techniques and methods including the genetic algorithm, neural networks, and collaborative filtering are utilized. The pre- and post-execution data show that the proposed framework changes the website structure based on the two criteria, i.e., sales conversion rate and average pre-purchase page views and finally leading to improved website usability.

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Authors and Affiliations

  1. Department of Information Technology Management, Faculty of Management, University of Tehran, Tehran, Iran

    Babak Sohrabi, Payam Mahmoudian & Iman Raeesi

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  1. Babak Sohrabi
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  2. Payam Mahmoudian
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Correspondence to Babak Sohrabi.

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Sohrabi, B., Mahmoudian, P. & Raeesi, I. A framework for improving e-commerce websites usability using a hybrid genetic algorithm and neural network system. Neural Comput & Applic 21, 1017–1029 (2012). https://doi.org/10.1007/s00521-011-0674-7

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