Book Recommender System

⁉️ Overview:

This project aims to develop a book recommender system using knowledge-based and reinforcement learning techniques. The system utilizes a dataset containing information about books, ratings given by users, and user demographics.

📈 Dataset:

The dataset comprises three CSV files:

1. Books.csv: Contains information about various books, including ISBN, title, author, publication year, publisher, and other attributes.
2. Ratings.csv: Includes data on user ratings for specific books, indicated by user IDs, book ISBNs, and ratings.
3. Users.csv: Contains user information such as IDs, demographic details (e.g., location, age), and possibly other user-related attributes.

🛠️ Implementation:

The project involves two main recommendation approaches:

🧠 1. Knowledge-Based Recommendation:

• Data Preprocessing: Null values are removed, and book attributes are converted to suitable data types.
• Creation of a User-Book Rating Matrix: A sparse matrix is constructed to represent user ratings for books.
• Similarity Calculation: Cosine similarity is computed between users based on their ratings.
• Recommendation Generation: Top-rated books by similar users are recommended to the target user.

🪖 2. Reinforcement Learning Recommendation:

• Data Preprocessing: Null values are handled, and data is preprocessed for reinforcement learning.
• Q-Learning Agent: A Q-learning agent is implemented to learn from user interactions and make personalized recommendations.
• Custom Environment: A custom environment is defined for the agent to navigate and learn from user feedback.
• Recommendation Process: The agent suggests books based on learned preferences and previous user feedback.

📂 Files Included:

• Presentation Slides: Contains an overview of the project, dataset, implementation details, challenges faced, and future improvements.
• Reinforcement-Learning.ipynb: Jupyter Notebook implementing the reinforcement learning-based recommendation system.
• Knowledge-Based.ipynb: Jupyter Notebook implementing the knowledge-based recommendation system.
• Books.csv: Dataset file containing book information.
• Ratings.csv: Dataset file containing user ratings for books.
• Users.csv: Dataset file containing user information.

⚙️ Requirements:

• Python 3.x
• Libraries: pandas, numpy, scikit-learn, scipy, gym

🕹️ Usage:

1. Ensure all required libraries are installed.
2. Open and run the Jupyter Notebooks (Reinforcement-Learning.ipynb, Knowledge-Based.ipynb) to view the implementation and results.
3. Explore the dataset files (Books.csv, Ratings.csv, Users.csv) to understand the data structure and attributes.