ECE 143 Group Project
Problem:
Building a recommender system for books
Dataset: Goodreads API: https://www.goodreads.com/api
The entire dataset contains information on the books, such as title, author, average rating, etc.
Proposed Solution and Real-World Application:
Our proposed solution is to use matrix factorization, neighborhood model, etc., to build a basic recommender system for books.
The real work applications of this solution are that it can be a recommender system that can help the users to choose books based on user ratings and preferences in different genres. Users can also get more information about what different genres have good reviews and ratings, and even a recommendation on a similar genre that they have liked in the past. The information from the dataset can also be used to recommend the user books from the same author they have liked in the past. The user can get a recommendation on the most popular English books versus other languages. We can extract and visualize information from the dataset like: what are the most popular books in each genre? What commonalities are there between these books? What books have the most engagement among Goodreads users?