Note to users:

"Use the prompt 'Write me a problem statement in one line based on the key features section' to ask AI to write the problem statement for you."

Problem Statement:

Develop a recommendation system that suggests books to users based on item-based collaborative filtering and popularity-based methods to enhance user experience and increase engagement.

Key Features:

• Item-Based Collaborative Filtering: Implemented an item-based collaborative filtering approach to calculate item-item similarities using cosine similarity, providing personalized book recommendations.
• Cosine Similarity Calculation: Used cosine similarity to measure the relationship between books, ensuring that users receive recommendations based on similar items they have liked.
• Popularity-Based Recommendations: Developed a popularity-based recommender that suggests the top 50 books with the highest average ratings, ensuring high-quality recommendations with a minimum of 250 ratings.
• Hybrid Recommendation Approach: Combined both item-based collaborative filtering and popularity-based methods to offer diverse and accurate recommendations, improving the overall recommendation quality.

Note to users:

"Use the prompt 'Write me 4-5 important tools and tech stack used in key features section' to ask AI to write the tools and tech stack section for you."

Tools and Tech Stack Used

• Python
• Pandas
• Selenium
• MongoDB