It’s safe to say that recommendation systems (also called recommendation engines) have become one of the most essential elements guiding users throughout the web.
Consider websites like Netflix:
The moment you watch a movie or search for one, Netflix remembers your choice and then provides you with recommendations based on what you’ve seen or looked for.
A well-designed recommendation system can process that data accurately and deliver predictions about the next film you’d like to watch or a product to buy. Software developers, data scientists, and businesses are all involved in collecting data and creating recommendation systems these days to improve the experience of users.
Python is a critical technology in recommendation systems today. Read on to find out why.
But first, what is a recommendation system?
We all intuitively know what a recommendation system is. But in technical terms, a recommendation system refers to a subclass of information filtering systems that are geared at predicting the rating or preference given to an item by a user. The system is designed to provide us with products based on our interests, past purchases, or other criteria.
Types of recommendation engines
Broadly speaking, there are 3 types of recommendation systems:
Simple recommenders – that type of recommendation systems offers generalized recommendations based on a product’s popularity or rating score. The idea behind it is that, in general, products that are more popular and get higher ratings from users hold a higher probability of being liked by the average audience.
Content-based recommenders – that type of recommendation systems suggests similar items on the basis of a particular item. The system uses metadata to provide these suggestions. For example, for proposing a movie it will use information such as the director, actors, genre, or description. This recommender system type assumes that if a user liked a particular item, he or she might also like an item which is similar – for example, a movie of the same genre or by the same director.
Collaborative filtering engines – these systems are designed to predict the preference or rating a user would give to an item on the basis of past preferences and ratings of other users. Contrary to content-based recommenders, collaborative filtering engines don’t require item metadata.
Once you identify the type of recommendation system you want to develop, you’ll need to find the right datasets to apply to it. There are a couple of such online datasets available for experimentation. However, you’re not there yet because first, you need to decide how you’ll be building your recommendation system.
Here’s why Python is an excellent language to support that type of project.
Why Python for recommendation systems?
Python is a popular interpreted language that in combination with machine learning has become one of the most common methods for building recommendation systems. Knowing Python is a huge advantage if you want to launch a career in data science today.
Here’s why Python is so good for building recommendation systems:
It’s easy to write and test code – since Python is such a productive language, it helps developers to write and test code easily. That, in turn, helps in dealing with sophisticated machine learning algorithms. Not to mention that it’s very flexible and integrating different types of data or applying data to an already existing operating system is relatively straightforward.
It offers handy libraries – a library is a collection of methods and functions that allow developers to perform plenty of actions without having to write their own code. Python offers a large number of libraries that help developers implement machine learning in their projects. Have a look at this list to see why Python makes it so easy to work with machine learning. https://datascienceplus.com/top-python-libraries-for-machine-learning/
It has a huge community – Python is surrounded by a community full of passionate and ambitious developers who are ready to help one another in different projects – including those related to machine learning. Moreover, Python is an open-source language, and there’s plenty of material available online that helps access knowledge relevant to machine learning easily.
That’s what makes Python a great pick for any project that focuses on building a recommendation system. Since you’re building a machine learning-based system, expect it to take a lot of time to develop and fine-tune. But it’s also worth to enjoy the learning process!
Have you got any questions about how Python comes in handy for building recommendation systems?
Reach out to us; we’re happy to help you pick the best technology for your project.