Recommendation System | Types of recommendation system | Data sparsity | Scalability | Cold start

Introduction 

It is the system that recommends something to the persons or the machine. For example, suppose a person purchase two books i.e; machine learning and python. Most probably that person also interested in visualization or analysis. Book seller recommend these books to that person.

Now a days we are surrounded with the lots of applicantions. And most of them are worked on recommendation system. Applications on -

E- commerce platforms
Amazon and other e commerce websites are using recommendations system to recommend their products to the users.

Social platforms
Linked in, facebook and other social media platform are also used recommendation system to recommend new connections , products etc.

Suppose according to your linked in profile, you are a software engineer and have number of connections related to similar domain. So linked in automatically reccomend profiles that have occupation as software engineer.

Types of recommendation system

1. Content based 

2. Collaborative filtering

Content based -

Content based recommendations system tries to recommend things to the user according to their profile, taste, preferences and their past activities. 

Suppose we have dataset of number of movies and all movies have different diffrent genre like action, horrar, comics etc.

And we have a user profile with his interest and rating for different different movies.

We want to recommend new movies to the users.

But which movie user like most. That can be done by recommendation system.

Suppose we have four movies  and user already liked first and second movies. So among third and fourth which movie user like most? Movie fourth is as same genre as movie first, and movie third is completely different genre. Most probably will user like movie fourth.

Collaborative filtering -

1. User based

2. Item based

In user based collaborative filtering, we took different user profiles having apporx same interest and preferences. And according to that preferences, It recommends products or movie to the target users.

In item baesd, we check the number of user records and check their previous reviews related to product, then compare their profile to the target user( for whom, we are going to recommend products) and then recommend products to the customer. 

Problems related to collaborative filtering

1. Data sparsity - It refers to the difficulty during finidng sufficient active users for recommend a product to the target users.

2. Cold starts - It is not possible to recommend the best things to the new users.

3. Scalability - Due to large anount of past preferences and reviews, it would be a challange  to recommend most appropriate products to the target user.

Advantage of recommendation system 

1. Broader exposure to thr users.

2. Tends to more selling and increase revenues.

3. Too many options for selecting a product.

I hope, now you have a better understanding about recommendation system and it's working. 

If you want a project on how to make recommendation system, write Project in comment section.

If you have any doubts, let me know in comment section or connect with me from the following link -

www.instagram.com/kavyansh.pandey

Thank you.