It is easy for us to get lost and confused with the vast amount of resources available to us in the internet. Fortunately, lots of websites and apps use search and recommendation engine to help us navigate our way around and sometimes even make the decisions for us. This article will try to explain in simpler detail what search and recommendation engines are all about.
What is Search Engine
Search engines allow us to search a database for information.
This is probably the most basic definition we can use to understand what search engine is. This article discusses in more detail how search engines work. For students however, it is probably just easier if to give you examples.
Examples of Search Engine
Google is probably the most common example of Search Engines. Whenever you come across an assignment which you have no idea about, you most probably rely on Google and its search bar. Gone are the days when students have to go to the library for their academic papers and their RRLs. As someone who has never used the library for its academic purposes, I can vouch for the power of Google.
However, the key to determining examples of Search Engines is not about thinking of websites or apps similar to Google, it is simply about thinking of websites or apps that use a similar tool – the search bar. Youtube, Netflix, and even Facebook all use search bars and therefore are all examples of Search Engines. Think of the e-commerce platforms or social media apps you use which have search bars somewhere in its interface, all these use Search Engine technology. This article gives some more common examples of search engines.
Recommendation Engine
The power of these platforms and apps does not simply rely on their search bars. The secret lies with their recommendation engine.
A recommendation engine suggests and matches products, services, and/or information to user preference based on analysis of data.
Have you ever seen phrases like, “Friends also liked…”, “People who bought this also bought…”, or “You may also want to try…”?
These phrases are the reason we waste more time and money than necessary in the internet. Just kidding. Or maybe not. (Wink)
Benefits of Recommendation Engine
It is all about improving the user experience. After watching a series or movie you liked in Netflix, the platform does an excellent job of suggesting similar shows which will successfully take up your whole schedule for the weekend. Thanks to their recommendation engine.
Google’s search bar sometimes does the thinking for us. Using data they have gathered around the world, they can predict what we are thinking and suggest auto fills on the topics we are searching for. Thanks to their recommendation engine.
E-commerce platforms are the worst. I meant best. They are the best salespeople in the world right now. They can suggest products for you to buy which you have never even thought of buying in the first place. And the crazy thing is, they make you think that you needed their suggested product all along! Thanks to their recommendation engine.
These are just examples of the magic of recommendation engine. Notice how there is a slight difference when it comes to how these examples use data to suggest and match user preference? That is because there are different types of techniques for Recommendation Engines.
Types of Recommendation Engine
There are two most common types of recommendation systems.
Content-based Filtering
Content-based filtering uses a single user’s data, yours, to recommend products, services, or information for you. This is very useful for platforms and apps to carefully and personally tailor-fit their user experience for you. However, as you can probably imagine, this can be limiting to users who want to explore experiences and preferences outside their usual tastes.
Collaborative Filtering
Collaborative filtering uses data from multiple users to suggest the best matches for your preferences. This helps us avoid one of the biggest tragedies in our lives nowadays: FOMO. For those not familiar what FOMO is, use a search engine to find out! (Wink)
On a more serious note, collaborative filtering helps companies expand the personal user experience for you and allows them to upsell you with their other products or services that are not necessarily within your initial preference bubble. However, this type of data analysis is harder to pull off and it requires a certain number of active users to draw inferences from.
Conclusion
Imagine yourself being pulled down the rabbit hole to the world of Alice in Wonderland. Search engines help you find out where exactly the exit is and how to get there. Recommendation engines will make the journey more exciting. (Wink)
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