Recommender system : What is it ??

 Have you ever wondered how come Facebook suggests you the friends whom you know somehow or you heard about them, or how come Netflix showing you different home screens with different movies you may like and you can not stop yourself to watch those movies, or blogging site like medium keep suggesting/showing you the blogs of your interest and keep you engage with the site, these all because of the very strong recommendation algorithm used by these sites.

In This case study, we’ll learn what is recommendation system?how it helps the various industry .

What is recommendation system ?

It is a machine learning algorithm that suggests relevant items to users, It is a kind of filtering system that seeks to predict the rating or the preference a user might give to an item and based on that predicted rating or preference system starts recommending the product to users, Eg: In the case of Netflix which movie to watch, In the case of e-commerce which product to buy, or In the case of kindle which book to read, etc.

Recommendation systems help to improve the user’s on-site experience by creating customized recommendations for every kind of user. This will give the user a better product search experience and will result in more user engagement on the site. There are 2 types of recommendation.

I. Content Based Filtering

In this type of recommendation system, relevant items are shown using the content of the previously searched items by the users. Here content refers to the attribute/tag of the product that the user like. In this type of system, products are tagged using certain keywords, then the system tries to understand what the user wants and looks in its database and finally tries to recommend different products that the user wants.

The advantage of this is that it doesn’t need data of other users since recommendations are specific to a single user and It makes it easier to scale to a large number of users.The model can Capture the specific Interests of the user and can recommend items that very few other users are interested in.

But there is also a disadvantage of this model in that it can only make recommendations based on the existing interest of a user. In other words, the model has limited ability to expand on the user’s existing interests.

II. Collaborative Filtering

Recommending the new items to users based on the interest and preference of other similar users is basically collaborative-based filtering. For eg:- When we shop on Amazon it recommends new products saying “Customer who viewed this also viewed” as shown below.

The advantage of this model is that it helps the users to discover a new interest in a given item but the model might still recommend it because similar users are interested in that item. But biggest disadvantage of this model is that It cannot handle new items because the model doesn’t get trained on the newly added items in the database. This problem is known as Cold Start Problem.

There are 2 types of collaborative filtering

a) User-Based Collaborative Filtering

Rating of the item is done using the rating of neighbouring users. In simple words, It is based on the notion of users’ similarity.

b) Item-Based Collaborative Filtering

The rating of the item is predicted using the user’s rating on neighbouring items. In simple words, it is based on the notion of item similarity.

Compiled by Bhumika Sharma