Recommendation Systems: Content-Based Filtering vs Collaborative Filtering

Abstract

A job seeker will spend hours searching for the most useful information while dealing with the vast volume of recruiting information available on the Internet. In today's technologically evolved world, the majority of internet users are continuously searching for various goods on the internet, and we normally do it via search engines. When searching, it's important to get the most relevant results possible, which recommender systems can help with. Users can easily find and evaluate items of interest when confronted with a huge number of possibilities. The goal of recommender systems is to establish a connection between products and consumers based on the users' interests. We evaluate and contrast the two basic approaches to recommendation systems in this paper. The first is referred to as Collaborative Filtering, and the second is referred to as Content-based Filtering.