Rule Engine vs Recommendation Engine

In one of the recent team discussions, I heard the terms Rule Engine and Recommendation Engine being used interchangeably. This was confusing, but understandable as some overlapping areas are there when we try to solve a problem where we are trying to take a decision based on given inputs. In truer terms, these are actually complementing technologies that can help reach a final decision in a complex situation.

What is a rule Engine?

Mostly based on a specific business scenario, where a set of rules can predict the outcome.

Use cases:

  • An insurer determines whether a candidate meets eligibility requirements.
  • A retailer decides which customers get free shipping and a discount.

Implementation: As a lot is written and talked about when it comes to the implementation of a rule Engine, rather than reinventing here, let me use an example


@Rule(name = "Hello World rule", description = "Always say hello world")
public class HelloWorldRule {

    public boolean when() {
        return true;

    public void then() throws Exception {
        System.out.println("hello world");

What is a recommendation Engine?

A very common feature you see in almost every website these days, let it be an e-commerce website recommending you products based on your past purchase history or an OTT site recommending videos to watch next. This takes into account users’ historical data plus data from other users with similar histories and tries to predict future likings. For example, users who like movies A, B, and C ended up looking a movie D.

Use cases

  • Recommend a video to be watched next
  • Recommend the next movie to be watched

Implementation Example-

Relationship between Rule Engine and Recommendation Engine?

Rules can be implemented on top of predictions coming from machine learning data. Reference Read