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Multi Clustering Recommendation System for Fashion Retail.

Pierfrancesco Bellini1, Luciano Alessandro Ipsaro Palesi1, Paolo Nesi1

  • 1DISIT Lab., University of Florence, DINFO dept, Florence, Italy.

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|January 19, 2022
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Summary
This summary is machine-generated.

This study introduces a novel recommendation system for fashion retail, utilizing multi-clustering to personalize customer experiences and boost retailer profits. It effectively addresses the cold start problem by predicting new customer behavior.

Keywords:
ClusteringCustomer and items clustering composedRecommendation systems

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Data Mining

Background:

  • The fashion retail sector experiences significant growth, necessitating advanced Customer Relationship Management (CRM) strategies.
  • Existing marketing solutions often lack personalization, focusing on general popular items rather than individual customer needs.
  • This gap highlights the need for customer-centric approaches to enhance shopping experiences and profitability.

Purpose of the Study:

  • To propose a novel recommendation system for fashion retail.
  • To enhance customer centricity and personalization in fashion marketing.
  • To address the cold start problem in recommendation systems.

Main Methods:

  • A multi-clustering approach was employed to group items and user profiles.
  • Data mining techniques were utilized to analyze customer behavior.
  • The system was developed and tested for both online and physical retail environments.

Main Results:

  • The proposed recommendation system effectively predicts the purchasing behavior of new customers.
  • The multi-clustering approach improved personalization beyond general marketing strategies.
  • Validation in real-world retail settings (Tessilform, Patrizia Pepe) demonstrated system efficacy.

Conclusions:

  • The developed recommendation system offers a personalized and customer-centric solution for fashion retail.
  • It successfully overcomes the cold start problem, enhancing user engagement and retailer profitability.
  • The system's validation confirms its practical applicability in both online and in-store scenarios.