Recommendation system is a tool that has seen major growth in the past few years owing to the success of many big organizations such as Amazon and Netflix. Almost every industry is now using a recommendation system for their website to create value for their business, to increase website traffic, increase ARPU and revenue. Recommendation systems have also been a great topic of discussion for developers who are constantly trying to better the older version. Did you know the first recommendation system was a ‘content recommendation system’.
What is a Content-Based Recommendation System :
A recommendation system that depends on the past behavior of users on the website and the similarities between items to create a recommendation list is known as a content-based recommendation system. It tracks everything that a user does on the website, including the products they click, like, and the time spent on each product. It also clusters all the items based on similarity. For example, blue-colored clothes come under a section of ‘blue clothes’. Content-based recommendation systems work best when the organization needs to avoid cold starts for any products they add to the website. As this recommendation system suggests products based on similarity, any new item added to the website can be listed in a list and recommended from there. One of the drawbacks of content-based recommendation systems is that it requires a lot of domain knowledge so that the recommendations made are 100 percent accurate; a large domain knowledge means that organizations have to buy a lot of storage.
How does a Content-Based Recommendation System Work?
A content-based recommendation system clusters all the items based on a similarity factor. It can be anything from colors to shape to genre. Also, one product can be in different lists as well. This data is usually updated every time a new item is added to the website. So, when a customer comes on the website and browses any item, the content-based recommendation system automatically starts analyzing the item attributes such as ratings, price, genre, classifications and then list down similar items based on the similarity. It then recommends these products based on their rating to the user. An example of a content-based filtering recommendation system –
Jenny watches Avengers, Iron Man, and Thor.
Now, the recommendation system will search for similar movies. The basis of similarity among the above movies can be that they are all Marvel movies and also action dramas.
So, recommendation engines will search for movies similar to this. It finds that Black Panther and Loki Series are both Marvel movies as well as action dramas.
So, the recommendation system will recommend ‘Black Panther’ and ‘Loki Series’ to Jenny.
Conclusion
A recommendation system is the need of the hour, and every industry must use this. But, it should also be kept in mind that recommendation systems don’t work on the formula of ‘one solution for every problem’. There are different algorithms in the recommendation system, and it needs to be used accordingly. So, what are you waiting for? Explore different recommendation systems and start with Alie – an AI-based recommendation system that uses NLP and machine learning to analyze the user’s behavior and then recommends products. Take its 14-day free trial to know more.
