Book Recommendation System

Working on not only recommending books but also using ML to recommend genres.

ran into some issues with the dataset I am using being too sparse so I tried some work arounds to use clustering to make the genres less sparse. It is still a work in progress, but I have added genre groupings using Kmeans and cosine similarity from averaged vectors. Both are still running into some issues stemming from the original dataset being quite sparse.

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.