Building a useful personalized recommendation system is not an easy task. There are many factors to consider in order to pick the right approach and make it work in particular business conditions. the paradigm is the same: if users with similar preferences to my own also like the same product, there's a high probability that I will like it too. The more data about your products and your users you have, the more precise the recommendations will be.
We've made a basic demo of a recommendation system to show you how it works. The easiest way to understand this is to show you using music recommendations. As a source of data we used an open source dataset from 2012 by Last.fm service. For the development we used Python, Streamlit, Numpy, Pandas and SQLike.
This approach can be used by any other industry. The idea behind the ML algorithm in the demo is to detect the dependencies of users’ preferences. If you type an artist name, the demo will recommend similar artists listened to by others who are also listening to this artist. Just enter your name and e-mail and check it out yourself.
