This repository contains code for building a recommendation system using the BERT model to extract meaningful representations from data and the K-Means algorithm for clustering and enhancing data analysis and recommendations.
Recommendation systems are widely used in various applications to suggest items or content to users based on their preferences and behavior. In this project, I leveraged the power of BERT, a state-of-the-art transformer-based model, to capture semantic relationships and understand the context of user preferences and product features.
- Utilizes BERT to extract meaningful embeddings from textual data.
- Applies K-Means clustering on the BERT embeddings to enhance data analysis and recommendation capabilities.
- Provides a recommendation based on the items present in the user's cart.