Netflips: A Content-Based Literature Recommendation System

Abstract

Recommendation systems for media can be roughly broken down into two categories: collaborative systems, which rely on similarities between users, and content-based systems, which rely on similarities between items available to be recommended. Most systems today are collaborative, including most book recommendation platforms, such as Kindle Unlimited or Goodreads. However, the capabilities of natural language processing techniques to automatically extract large amounts of information from texts makes a content-based approach for book recommendation viable. This paper describes the implementation of a prototypical content-based book recommendation system, recommending a selection of books from Project Gutenberg’s public domain corpus. Based on preliminary results from a handful of informal test cases, this system was at least somewhat successful, providing satisfactory recommendations more than half of the time. It struggles with genre and subject matter alignment, though this could be mitigated through the incorporation of more abstract NLP techniques (such as topic modeling) into the feature extraction process. The fact that it is successful at all, while still being fairly rudimentary, indicates that content-based recommendation systems for literature have real potential.