Book Recommender System Using Collaborative Filtering
Sukanya Kool, Abhijit Paul, Tamoghna Mukherjee · 2025
This study explores the development of a book recommender system employing collaborative filtering, a widely used technique in recommendation systems within the broader field of artificial intelligence. The investigation covers fundamental aspects such as the definition, tools, and types of both machine learning and recommendation systems. Recommendation systems have become integral to daily life, with widespread applications. This research specifically focuses on collaborative filtering, utilizing tools such as SciPy and Scikit-learn. A thorough analysis of these tools and algorithms is conducted to optimize precision, ultimately leading to the development of a robust book recommendation system. The implementation process involves key steps, including data gathering, data cleaning, model building, model training, model testing, and model optimization. The chapter concludes with an evaluation of the tools’ efficiency, insights gained from the project, and suggestions for future work in similar system domains. This work contributes to a broader understanding of collaborative filtering and provides practical insights into the development of effective recommendation systems.