Building a Recommender System with Similarity Metrics

Photo by Pawel Czerwinski on Unsplash

I. Introduction

The vast content repositories of online streaming platforms, such as Netflix, necessitate the development of effective recommendation systems. By offering personalized movie suggestions based on users’ historical interactions, these systems can significantly enhance customer satisfaction and, consequently, revenue. The techniques explored here extend beyond movies and are applicable to various items where personalized recommendations are valuable.

While search engines address some level of information retrieval, they often fall short in solving the personalization problem. Recommendation systems play a crucial role in shaping user experiences across various domains, from e-commerce to video-on-demand services. These systems, categorized as information filtering tools, aim to provide sensible suggestions to users based on their preferences.

II. Types of Recommendation Systems:

Recommendation systems primarily employ three approaches:

1. Demographic Filtering:

1. Offers generalized recommendations based on the popularity and genre of items. It tends to be simplistic as it recommends the same items to users with similar demographic features.