The Age of Personalized User Experience: Recommendation Systems

In today’s modern society, we are entering a data collection era, everything from our preferences to our search histories are stored and used to recommend us related items.

For example, YouTube recommends us what videos to watch next. Amazon recommends related items to add to our shopping cart and Facebook suggests new people you might be interested to follow.

Recommendation systems exist almost everywhere these days. So in this article, I’d like to briefly explain how they work as I recently read some fascinating articles on it. Note that I am not going deep into implementation because I want to keep this article as an overview rather than a step-by-step guide.

Why are they used?

For most businesses, knowing their customers can contribute to a significant amount of success and profitability. Understanding what each user likes or dislikes can ensure that their products can stay relevant and competitive in the market.

Recommendation systems provide a good solution to predict which products their users are more likely to consume, and which ones are less attractive for them. Hence, by recommending products more suitable to each user, a company can have a better chance to survive and sustain.