Multi Clustering Recommendation System for Fashion Retail

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.