Recommendation System Architecture Basics

Most recommendation systems combine collaborative filtering, which looks at what similar users liked, with content-based filtering, which looks at the attributes of the items themselves.

A cold-start problem arises for new users or new products with no history, which is usually addressed with simple popularity-based recommendations until enough data accumulates.

We start clients with a simpler hybrid approach before investing in more advanced deep learning recommendation models, since the simpler system often captures most of the value at a fraction of the cost.