If we want to create a recommendation system for a service or platform we can approach the goal in many ways. Looking at the current trends, in particular the fashion for deep learning, we may be impressed that the use of only very complex algorithms will result in success. However, it is not so. We can apply recommender systems in scenarios where many users interact with many items and the system will help to suggest items that have been hosted by users that are similar.
The challenge with more complex approaches is that they can be sometimes difficult to tune and interpret. In other words, they can be very powerful but require a lot of knowledge to implement properly. Associations analysis from another side is relatively light on the math concepts and easy to explain to laypeople. In addition, it is an unsupervised learning tool that looks for hidden patterns so there is a limited need for data prep and feature engineering. It is a good start for certain cases of data exploration and can facilitate more insightful approaches to data.