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Price comparisons on the internet based on computational intelligence.

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This study introduces an intelligent product search system to improve online price comparisons. It uses fuzzy logic for flexible price ratings and personalized recommendations to help shoppers find products easily.

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Area of Science:

  • Information Science
  • Computer Science
  • E-commerce Technology

Background:

  • Information-intensive Web services like price comparison sites are increasingly popular.
  • Users, especially novices, struggle with information overload and uncertainty on these platforms.
  • Existing price comparison sites have limitations, necessitating innovative solutions.

Purpose of the Study:

  • To develop an intelligent product search system for more effective online price comparisons.
  • To enhance the user experience for online shoppers navigating complex product information.
  • To address the challenges posed by information overload in e-commerce.

Main Methods:

  • Implementation of a novel intelligent product search system.
  • Adoption of linguistic price ratings utilizing fuzzy logic for user-defined price ranges.
  • Personalization of product recommendations based on linguistic product clusters.

Main Results:

  • The developed system facilitates more effective price comparisons for online shoppers.
  • Fuzzy logic enables flexible accommodation of user-defined price preferences.
  • Linguistic product clustering enhances the convenience of finding desired items.

Conclusions:

  • The intelligent product search system offers a more effective approach to online price comparison.
  • Fuzzy logic and personalized recommendations improve shopper convenience and product discovery.
  • This system addresses key usability issues in information-intensive e-commerce environments.