Understanding basics of recommender systems in Python | EduGrad

We always wonder how Amazon recommends products to different users to buy online.

How On-demand Video platform such as Netflix recommends movies to its subscribers to watch?

How apps such as Podcast and Spotify recommend music to its users to listen?

Let’s understand what is Recommendation System?

There is no magic, they use the recommendation system algorithm to make this happen. With the growing amount of data and with a significant rise in the number of users, it becomes increasingly important for companies to search, map and provide users with the relevant chunk of useful information according to their preferences and tastes.

Imagine a scenario when a user needs to buy something from an online store. Due to the tremendous amount of data, a user may not be able to find his/her required information and thereby would leave the website thereby decreasing the user experience of the website.

This is called Information Overload.

Understanding basics of recommender systems - recommendation system machine learning | EduGrad

This leads to the loss of the companies and to overcome this situation, recommender systems are used.

Recommender systems are simple algorithms that aim to provide the most relevant and accurate items to the user by filtering useful stuff from a huge pool of information base. Recommendation engines discover data patterns in the data set by learning user’s choices and produces the outcomes that co-relates to their needs and interests.

Understanding the basics of Recommender Systems - EduGrad

 

We can say that most general and basic definition of a recommendation system is “Recommender systems are systems that help users discover items which they may like”

There are mainly two types of recommendation system – 

  1. Content-based recommendation system: –

Content is defined as the attributes of an item. So content-based methods are based on the description of an item and profile of the user’s preferences. It also recommends items similar to those which a given user has liked in the past.

The content-based recommender systems take into consideration the attributes or descriptions of items and items with similar descriptions are recommended together.

content based recommendation system in Python

The user’s choices such as which items he is interested in. Which categories of items he like the most.

Say if a user likes watching action movies and doesn’t like watching horror movies then the recommender systems would recommend him only action movies and no horror movies would be recommended as the content or description of each and every movie is processed and movies similar to action movies are being recommended.

Based on that data, a user profile is generated, which is then used to make suggestions to the user.

2. Collaborative-based recommendation system:-

collaborative filtering recommender systems - movie recommendation system | EduGrad

Collaborative filtering doesn’t need anything like content-based filtering.

It requires users’ historical preference on a set of items. Because it’s based on historical data, the core assumption here is that the users who have agreed in the past tend to also agree in the future.

In terms of user preference, it usually expressed in two categories.

Explicit Rating: – It is a rate given by a user to an item on a sliding scale, like 5 stars for Titanic or a thumbs up. This is the most direct feedback from users to show how much they like an item.

Implicit Rating: – It suggests the user’s preference indirectly, such as the number of clicks, time spent on a particular item, items added to the wish-list and items added to cart.

Then users with similar interests are clubbed together and items liked or watched by one user are recommended to the second user.

That’s why it is called collaborative filtering since the collaboration of different users is taken into account.

In simple words, say there are two users A and B.

User A watched movies such as Avengers, Avengers-Age of Ultron and Avengers-Infinity War. User B, on the other hand, watched Avengers and Avengers-Infinity war. Since we can say that both user’s interest is similar then items watched by user A are being recommended to User B as they are sharing similar interests.

Therefore, the movie Avengers-Age of Ultron is being recommended to User B.

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