August 18th 2023

                                                        Supriya Arun

My personal notes as I navigate understanding recommendation systems in depth

Preface : I am fairly new to NLP and recommendation systems. My goal is to understand recommendation systems in depth and in this process make my notes available here. I refer to courses, papers and video lectures that are referenced in the end.

Survey Paper : Deep Learning based Recommendation System : A survey and New Perspectives

Shuai Zhang, Lina Yao, Aixin Sun, Yi Tay

I picked this as my first material to read for a couple of reasons:

• the paper is fairly recent, published in 2019 so it would cover most of the notable milestones in recommendation systems
• just browsing through the pages showed me that though its a 28 page survey paper, the paper had a gradual progression through topics I am familiar with (CNN, Auto Encoders, MLP) to topics that I am yet to learn about
• there are a number of topics discussed, hence the paper has good breadth and would serve as a good introduction to the topic without putting me into a rabbit hole for each topic

What are Recommendation Systems?

In one sentence, “Sell Products you could never sell before to users you could never sell to before”. In many sentences :

• Identify things users like
• Suggest new items, suggest items on topics you like
• Provide a way to serve new content to users that they might have never thought of searching but are still relevant to them

Why use Recommendation Systems?

Recommendation systems were first developed based on a simple observation that individuals often rely on recommendations provided by others in making daily decisions.