Introduction to recommender systems - one stop shop

Hello All,

welcome to my blog, if you want to find out more about personalization aka recommender systems which many companies utilize to provide you with personalized content (like Netflix, Google, Amazon, and many others) keep reading. (To jump right into reading scroll below to "Content").

Shortly about me: 

Recommender system developer and lead - linkedin for more details: https://www.linkedin.com/in/vojtech-kral-48a64942
Feel free to contact me on linkedin with any curious questions or professional opportunities. There is absolutely no boring discussion for me when it comes to recommender systems :). 

Why I started this blog?

In 2014 I discovered the domain of recommender systems. I found the beginnings quite challenging firstly because I am coming from Software engineering background and not from a data science background or pure math background and secondly because I was struggling to find any "overview" material. I ran into a lot of scientific papers that are, of course, awesome but they were usually focusing on one specific part. I was struggling to find some high level overview that would start from generics first and then go deeper. After two years in this field I have still tons to learn but I think I have gathered enough information to give you starting hints to you who might be at the same spot as I was few years ago.

This will be a series or posts where I will cover 360 degree overview of recommender systems to get you the starting information. I want to make this series a one stop shop  for starters full of information I found myself until now, heard at the conferences, learnt from papers/books etc.

Here is the list what I will be covering in the series:

• high level principles what the recommender systems are based on
• math
• evaluation
• Software architecture
• other connected areas such as, explanations, user experience
• industry cases I read or heard of will be mixed in

Disclaimer: I have not done any academic research on my own in this field. 

Content:

Posts composing the series:

1. Is there a definition?
2. When and why we started talking about RS?
3. Are they used anywhere?
4. Personalized vs. non-personalized 
5. Implicit vs. explicit feedback

My presentations on youtube:

• Recommended for you  (16.5.2016 - Informatics Evenings Lecture Series, FIT, Czech Technical University)

Latests post outside of the series:

• Recommender systems enforce filter bubble, or not really?
• Article on A/B testing 
• Books on recommender systems 
• ... Just click on the "Recommender systems" topic on the right of the page and you see all of them.

Coming posts:

• Content based method
• Collaborative filtering method
• Matrix factorization method
• Java and matrix factorization method - what to use?
• Evaluation - offline/online 
• ...