This book is a comprehensive and engaging exploration of the captivating world of recommendation systems, where we uncover the secrets behind personalized movie recommendations that keep us hooked to our screens. In today's digital age, recommendation systems are the backbone of our online experiences, providing us with tailored suggestions for movies, music, books, shopping, and more. These intelligent algorithms have become indispensable, enriching our lives by catering to our unique preferences and tastes.
This book takes readers on an engaging journey, covering the importance of recommendation systems; various types, including collaborative filtering, content-based filtering, and hybrid approaches; real-world applications and impact; Python setup and libraries; data set exploration; data pre processing and feature engineering, such as handling missing data, outliers, and text processing; content-based filtering with TF-IDF and word embeddings; collaborative filtering techniques like user-based, item-based, memory-based, and model-based approaches; evaluation metrics like precision, recall, F1-score, RMSE, and result interpretation; and concludes with a comprehensive step-by-step guide on building a simple movie recommendation system using rotten tomato data set, embracing a project-based learning experience.
It is designed to cater to a diverse range of readers. Whether you are a data science enthusiast, a curious learner seeking to understand the inner workings of recommendation algorithms, or an experienced data professional looking to enhance your skillset, this book has something to offer you. The content spans from introductory concepts to advanced techniques, ensuring that every reader finds value in their pursuit of knowledge.
