Using parts of the paper without referencing is considered as copyright infridgement. You can reference me by {X. Jin, "How to optimize product recommendations using machine learning", Scientific Paper Class 2, Darmstadt University of Applied Science, June 2022} + a link to this github repo.
Abstract: In the last two decades, e-commerce has developed itself from a niche market to one of the most prominent players in product distribution. Nowadays, online shops offer an enormous amount of products to their customers. With a large amount of data, it has become a challenge to process and provide valuable data to customers to fulfill their needs and choice of products. Big players like Amazon have been using recommendation engines with machine learning algorithms like collaborative filtering, hybrid filtering, content-based filtering, etc., to overcome real-time product recommendations for their customers. This paper will look at state-of-the-art recommendation engines and their algorithms. We will determine how to optimize a recommendation engine which recommends consumers quality products.
A more detailed look into the implementation can be found in chapter 3 & 4.
https://jmcauley.ucsd.edu/data/amazon/
More information regarding the conducted survey can be found in the chapter 5.
