Content Based Filtering And Collaborative Filtering: A Comparative Study

Abstract

Collecting data from users is a frequent practice for websites to improve various aspects of their products and services, such as performance, usability, and security. Monitoring user activity on websites helps to comprehend visitor behavior and assess the impact of the site. Numerous applications involve the collection of user data by websites, enabling the prediction of user preferences. This, in turn, facilitates personalized content recommendations. Recommender systems serve as a mechanism to propose analogous items and concepts tailored to an individual's unique mindset. Fundamentally, there are two categories of recommender systems: Collaborative Filtering and Content-Based Filtering. This paper provides a comparative study of collaborative filtering and content-based filtering.