A furniture recommendation system is an intelligent software program that suggests furniture items to users based on their preferences, needs, and budget. Recommendation systems are a type of artificial intelligence technology that provide personalized suggestions to users based on their past behavior, preferences, and other relevant data. Furniture recommendation systems are designed to enhance the user's shopping experience by providing personalized and relevant product recommendations. By reducing the time and effort required for users to search for furniture items, these systems can improve customer satisfaction and increase sales for businesses. Some challenges of furniture recommendation systems include dealing with incomplete or inaccurate user data, managing the diversity of furniture items, and balancing the need for personalized recommendations with the need for serendipity and exploration. However, with advances in data science and artificial intelligence, furniture recommendation systems are becoming increasingly sophisticated and effective .This project is all about recommendation engines.
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Furniture Recommendation system based on collaborative filtering methos using Transfer learning and machine learning algorithams.
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