Recommendation engines are all the rage. From Netflix to Amazon, all of the big guys have been pushing the envelope with research initiatives focused on making better recommendations for users. For years, most research appeared through academic papers or books that neatly organized these papers into their respective techniques (e.g. collaborative filtering, content filtering, etc.) to make them easier to digest. There have actually been very few pure text books on the subject given it is a fairly new research area.
In 2016,
Charu Aggarwal
published
Recommender Systems: The Textbook
, a massively detailed walkthrough of recommendation systems from the basics all the way to where research is at today. I highly recommend it to anyone interested in recommendation systems, whether you are doing research or just want to gain some intuition, his explanations are fantastic!
In chapter 3 of his book, Aggarwal discusses model-based collaborative filtering, which includes several methods of modelling the classic user-item matrix to make recommendations. One focus of the chapter is on
matrix factorization
techniques that have become so popular in recent years. While introducing unconstrained matrix factorization, he remarks the following:
Much of the recommendation literature refers to unconstrained matrix factorization as singular value decomposition (SVD). Strictly speaking, this is technically incorrect; in SVD, the columns of U
U
" role="presentation" style="box-sizing: border-box; display: inline; line-height: normal; word-spacing: normal; word-wrap: normal; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border-width: 0px; border-style: initial; border-color: initial;">
