Recommendation engines are arguably one of the trendiest uses of data science in startups today. How many new apps have you heard of that claim to “learn your tastes”? However, recommendations engines are widely misunderstood both in terms of what is involved in building a one as well as what problems they actually solve. A true recommender system involves some fairly hefty data science — it’s not something you can build by simply installing a plugin without writing code. With the exception of very rare cases, it is not the killer feature of your minimum viable product (MVP) that will make users flock to you — especially since there are so many fake and poorly performing recommender systems out there.
A recommendation engine is a feature (not a product) that filters items by predicting how a user might rate them. It solves the problem of connecting your existing users with the right items in your massive inventory (i.e. tens of thousands to millions) of products or content. Which means that if you don’t have existing users and a massive inventory, a recommendation engine does not truly solve a problem for you. If I can view the entire inventory of your e-commerce store in just a few pages, I really don’t need a recommendation system to help me discover products! And if your e-commerce store has no customers, who are you building a recommendation system for? It works for Netflix and Amazon because they have untold millions of titles and products and a large existing user base who are already there to stream movies or buy products. Presenting users with recommended movies and products increases usage and sales, but doesn’t create either to begin with.
There are two basic approaches to building a recommendation system: the collaborative filtering method and the content-based approach. Collaborative filtering algorithms take user ratings or other user behavior and make recommendations based on what users with similar behavior liked or purchased. For example, a widely used technique in the Netflix prize was to use machine learning to build a model that predicts how a user would rate a film based solely on the giant sparse matrix of how 480,000 users rated 18,000 films (100 million data points in all). This approach has the advantage of not requiring an understanding of the content itself, but does require a significant amount of data, ideally millions of data points or more, on user behavior. The more data the better. With little or no data, you won’t be able to make recommendations at all — a pitfall of this approach known as the cold-start problem. This is why you cannot use this approach in a brand new MVP.