Board Game Recommendation System with Collaborative Filtering

Abstract

This paper presents the building of a recommendation system, based on collaborative filtering, using real data about board games and their ratings from users. It reviews the notion of web scraping, data cleaning and recommendation systems, and the used technology for development. Describes the building steps of a python-based application. Shows how to collect data using web scraping technique. Also mentions how to prepare, analyze, and visualize the gathered data with popular python libraries.