Recommender systems are among the most popular applications of data science today. You can apply recommender systems in scenarios where many users interact with many items. Recommender systems recommend items to users such as books, movies, videos, electronic products and many other products in general.
One key reason why we need a recommender system in modern society is that people have too much options to use from due to the prevalence of Internet. In the past, people used to shop in a physical store, in which the items available are limited. By contrast, nowadays, the Internet allows people to access abundant resources online. Netflix, for example, has an enormous collection of movies. Although the amount of available information increased, a new problem arose as people had a hard time selecting the items they actually want to see. This is where the recommender system comes in.

Recommender systems play a major role in today’s ecommerce industry. Almost every major tech company has applied recommender systems in some form or the other. Amazon uses it to suggest products to customers, YouTube uses it to decide which video to play next on autoplay, and Facebook uses it to recommend pages to like and people to follow. For some companies like Netflix and Spotify, the business model and its success revolves around the potency of their recommendations. In order to develop and maintain such systems, a company typically needs a group of expensive data scientist and engineers. Recommendation systems are important and valuable tools for companies like Amazon and Netflix, who are both known for their personalized customer experiences. Each of these companies collects and analyzes demographic data from customers and adds it to information from previous purchases, product ratings, and user behavior.These details are then used to predict how customers will rate sets of related products, or how likely a customer is to buy an additional product.

Companies using recommender systems focus on increasing sales as a result of very personalized offers and an enhanced customer experience. Recommendations typically speed up searches and make it easier for users to access content they’re interested in, and surprise them with offers they would have never searched for.The user starts to feel known and understood and is more likely to buy additional products or consume more content. By knowing what a user wants, the company gains competitive advantage and the threat of losing a customer to a competitor decreases.Furthermore, it allows companies to position ahead of their competitors and eventually increase their earnings.
There are different type of recommender systems such as content-based, collaborative filtering, hybrid recommender system, demographic and keyword based recommender system. Variety of algorithms are used by various researchers in each type of recommendation system. Lot of work has been done on this topic, still it is a very favourite topic among data scientists.
Data is the single most important asset for building a recommender system. Essentially, you need to know some details about your users and items. The larger the data set in your possession, the better your systems will work. It’s better to have a basic recommender system for a small set of users, and invest in more powerful techniques once the user base grows.
As more and more products become available online, recommendation engines are crucial to the future of e-commerce. Not only because they help increase customer sales and interactions, but also because they will continue to help companies weed out their inventory so they can supply customers with products they really like.