Big Data & Microservices - Know How?

Recommender Systems — User-Based and Item-Based Collaborative Filtering This is part 2 of my series on Recommender Systems. The last post was an introduction to RecSys. Today I’ll explain in more detail three types of Collaborative Filtering:  User-Based Collaborative Filtering (UB-CF) and Item-Based Collaborative Filtering (IB-CF). Let’s begin. User-Based Collaborative Filtering (UB-CF) Imagine that we want to recommend a movie to our friend  Stanley . We could assume that similar people will have similar taste. Suppose that me and  Stanley  have seen the same movies, and we rated them all almost identically. But Stanley hasn’t seen  ‘The Godfather: Part II’ and I did .  If I love that movie, it sounds logical to think that he will too. With that, we have created an artificial rating based on our similarity. Well, UB-CF uses that logic and recommends items by finding similar users to the  active user  (to whom we are trying to recommend a movie). A specific applicati

How Recommender systems works (Python code — example film Recommender) Nowadays we hear very often the words “ Recommender systems ” and mainly it’s because they are quite often used by companies for different purposes, such as to increase sales (items’ suggestion while purchasing → Amazon: user that have bought this as also bought this) or in suggestions to customers to give them a better customer experience (film suggestion → Netflix) or also in advertising to target the right people based on preferences similarities. The recommender systems are basically systems that can recommend things to people based on what everybody else did. Here there is an example of film suggestion taken from an online course. I want to thank  Frank Kane  for this very useful course on Data Science and Machine Learning with Python. Here there is the course’s  link  in case you would like to go deeper with Data Science. We’ll make an example taking the database provided in the course, because it’s