Book Recommendation System

The recommendation system now recommends books based on a list of favorite books. Additionally, I began adding a clustering feature for genres, but this has not been finished yet.

I began work on my Book Recommendation System.

First found a dataset off of Kaggle with a comprehensive collection of information on each book. I wanted to do content based filtering so I made sure the dataset had descriptions and genres for each book.

First, I cleaned up the data, created a “combined features” column that took all the string data and combined it. Any book that didn’t have the string data was disregarded.

Then I used TFIDF Vectors and Cosine Similarity to find relating books.

The similarity matrix was also visualized with Matplotlib.