H&M Recommendation System

Problem Statement: The fast-fashion industry is facing the challenge of predicting customer preferences accurately, deviating from the traditional approach of trend creation. To address this, H&M proposes the implementation of a personalized fashion recommendation system. However, a key issue lies in the need to optimize the model's accuracy and relevance to individual users. Additionally, there is an opportunity to expand beyond clothing recommendations to encompass lifestyle products, leveraging the potential of a big data network.

Proposed Solution: The recommended solution involves the development and implementation of an advanced recommendation system for H&M, utilizing customer purchase data and a machine learning model. By recommending items similar to those previously high-rated or frequently purchased by a customer, the system aims to enhance user satisfaction and contribute to a continuous improvement of the model's accuracy. The bi-directional benefits ensure that as more customers engage with the system, it gathers valuable data to further personalize user experiences. The scalability of the system, supported by hosted server utilities and deep learning frameworks, allows for efficient processing of vast datasets. Moreover, the system's maintainability is ensured through a step-by-step guideline based on a baseline model, and its adaptability allows for migration to various platforms, including websites or apps, with the support of cloud computing. The proposed two models, based on product features and customer purchase behaviors, provide a comprehensive approach to product recommendations, ensuring reliability through a substantial real-world transaction dataset of 34GB. This solution not only addresses the current challenges in the fast-fashion industry but also opens avenues for future growth and diversification into lifestyle product recommendations.