Tips, Expertise, Articles and Advice from the Pro's for Your Website or Blog to Succeed
This post will share my knowledge about unsupervised learning in data mining with the simplest algorithm, which we used to generate associated rules to determine the related grocery items customers bought from our e-commerce application/retail stores.
Before jumping ahead, Let’s understand a few terms which I will be using in this article.
Recommender systems promise to reduce churn and increase sales. But how do you measure their actual success? What is it that you should measure? And what challenges should you look out for when you’re building your recommendation engine? In this article, I’ll discuss some challenges of recommendation engines, the ROI, and standard metrics to help evaluate their performance.
You might also like: Building a Recommendation System Using Deep Learning Models
Most articles about recommendation engines focus on all the bright sides of recommendations: personalized customer experience, lower churn, increase in sales, and more revenue. While all of that is true, as we can see looking at the examples of numerous companies including Amazon, adopting a new technology requires a strategic approach — so you should be realistic and well-prepared and not only optimistic about the future outcomes. There are some challenges that you have to be aware of.