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Much has already been said about data science and its importance in various businesses. We live in a world where almost every business decision we take is based on careful and scientific analysis of data. Data may have come a long way, but the truth is that it has barely begun. There is a massive potential in the field of data science and related areas like machine learning; of which Recommendation Systems happen to belong to one of its various use cases.

Each time we’re shopping online, watching YouTube videos, browsing Netflix, streaming music, and even typing a note on a smartphone, whether we realize it or not, we are contributing to and taking advantage of recommendation system algorithms.

Everything is online today. From people’s friends’ list, buying behavior, pictures, what they like and what they dislike, their opinion about a particular thing or topic, etc. All these details (data) about individuals can be scientifically analyzed and used in creating a better online environment. From suggesting the books, movies, and videos, they might like or an item they might be interested in buying.

So What is the Recommendation System all about?

A Recommendation System refers to a system that is capable of predicting the future preference of a set of items for a user and recommends the most relevant items to the user.

Previously, people solely rely on their family members, friends or experts to advise them on what movies to see, the books to read, the music to stream – but today, things have changed drastically! People now rely on match-predictive algorithms so often that they may not even notice it.

Recommendation Systems recommend items to users such as books, movies, videos, electronic products, and many other products in general. Netflix would be an example of this, it uses the data of millions of users regarding the movies and shows they’ve already seen, the actors they like and the kind of movies they like. After running an advanced algorithm (part of data science) on this data, they come with the list of movies or shows an individual will be more interested in watching and they start “suggesting” them these movies.

You may have already noticed the same thing on YouTube when you watch a few videos on YouTube; boom – YouTube automatically starts suggesting you more videos related to what you’ve viewed previously. This may look very simple, but there are complex algorithms running in the background that makes this possible.

So, why should anyone care about building a recommender system, and most importantly, why should you?

Retargeting marketing is now the most prominent marketing trend that brings in a good return on investment because of the insight from data. Netflix values the recommendation engine powering its content suggestions at $1 billion per year, and Amazon says its system drives a 20-35% lift in sales annually. What makes these systems so valuable? The answer lies in data science that powers them.

One key reason why we need a recommender system in modern society is that people have too many options to use due to the vastness of the Internet.

Have you ever bought a product online and the next thing you see are recommended products relating to exactly the products you just bought? It is always tagged: “Products you may also like” Well, the application of Machine learning algorithm, a specialized field in data science is used to make this accurate prediction and recommends your next products. As a business owner, you can as well leverage on the almighty power of Artificial Intelligence (AI) like building a chatbot for your business, and Machine Learning (ML) by taking advantage of technology’s ability to streamline data, unlock user interest and engage your users in a highly relevant way.

Types of Recommendation Systems

There are different types of recommender systems such as:

• Content-based
• Collaborative filtering
• Hybrid recommender system,
• Demographic and keyword-based recommender system.

Varieties of algorithms are used by various researchers in each type of recommendation system depending on the use case.

Recommendation systems with robust algorithms are at the core of today’s most successful online companies such as Amazon, Google, Netflix, and Spotify. By endlessly recommending new products that suit their customers’ tastes, these companies provide a personalized, attentive experience across their brand platform, effectively securing customer loyalty.

In summary, recommender systems help the users to get personalized recommendations, allows users to take correct decisions in their online transactions. As a business owner, it can help you increase sales, redefine your customer’s web browsing experience, retain your customers, enhance your customer’s shopping experience, and much more.

Chances are you’re interested to know how to build a recommendation system for your business. If you have any experience in Data Science or programming, this article will pretty much give you a headstart on how to go about it. If you’re however unfamiliar with data science or programming, you can contact us to help you build one for your business.