Project on Recommendation Engine - Book Recommender

Overview

This hands-on project-based course guides learners through the process of designing, developing, and evaluating a functional Book Recommendation Engine using Python and data science techniques. Beginning with foundational principles, learners will identify key components of recommender systems, prepare structured datasets, and apply user-driven filters to generate personalized recommendations. In the advanced stages, learners will construct content-based filtering models using textual data, extract meaningful features with TF-IDF and Count Vectorizers, and compute similarity scores to rank items effectively. Throughout the course, learners will also integrate, combine, and transform multi-attribute metadata (e.g., author, title, genre) to enhance the relevance of outputs. By the end of this course, learners will be able to design, implement, and refine a real-world recommendation engine that simulates industry-standard systems.

Syllabus

Foundations of Book Recommendation System

This module introduces learners to the core principles of building a book recommendation engine using user-defined filters and structured data. Learners will explore initial project setup, data preprocessing techniques, and the application of foundational filtering logic based on publication metadata and user preferences.

Building and Enhancing the Recommendation Engine

This module advances learners into content-based filtering techniques by leveraging text features such as book title, genre, and description. Through the construction of similarity matrices and feature combination strategies, learners will implement a more intelligent and personalized recommendation engine.