Recommendation Systems
What are recommendation systems?
A recommendation system is a type of information filtering system that suggests items or products to users based on their preferences, interests, and behavior. These systems use various data mining and machine learning algorithms to analyze users' historical data and recommend the most relevant items that they are likely to be interested in. Recommendation systems are widely used in various industries, including e-commerce, social media, entertainment, and more. They help improve user experience, increase customer satisfaction, and drive sales and revenue for businesses.
What is the utility recommendation systems?
Recommendation systems have become an essential component of many industries, including e-commerce, streaming services, and social media. According to a report by MarketsandMarkets, the global recommendation engine market is expected to grow from $1.21 billion in 2017 to $4.45 billion by 2022, with a compound annual growth rate (CAGR) of 29.7%. Additionally, a study by McKinsey found that 75% of what people watch on streaming platforms such as Netflix and Amazon Prime Video comes from recommendations made by the platform's recommendation algorithm. This highlights the significant impact and utility of recommendation systems, which can increase customer engagement, satisfaction, and ultimately revenue for businesses.
Why this repository?
This repository contains recommendations system for various business types including sports, entertainment, and healthcare, among others.