An In-Depth Guide to Machine Learning Recommendation Engines
Recognize these prompts: “Recommended for you”, “Others also viewed”, “You may also like…”? If so, chances are that you use recommendation systems every day. Or, to be more accurate, they serve you, even if you’re not aware.
Recommender systems are an inseparable part of any medium-sized or big e-commerce website. The system’s role is to suggest relevant new content to help users find exactly what they seek. A good recommendation boosts sales, AOV and conversion rates, because it generates automatic content advice based on user’s preference.
This thorough guide will help you navigate through the complex mechanics of recommender systems. Here’s why they are able to figure out what the customer “may also like”.
In order to make an informed suggestion, a recommendation system has to understand as much as possible about the user’s needs.
How does the recommendation engine work? It analyzes website traffic and content to identify the most popular or relevant products for visitors. But, to dig a little deeper into what a particular user wants, it requires behavioral and statistical data.
