Building a Recommender System for Data Enrichment
What are Recommender Systems?
You’ve seen it everywhere. Every big tech company is now using some kind of Recommender System in their platform. Facebook suggests friends, Netflix recommends movies, same with Youtube and videos and Amazon recommends… well everything. Why do they do it? Well, every case is different but in general, good recommendations mean a better user experience and they will find new ways to keep users engaged and satisfied with the service. Happy users equal recurring users.
But should we implement a recommender system just because all the cool kids are doing it? We need to think about our business needs and determine whether or not a system like this will add value to our service. At Explorium we provide our users with thousands of data enrichment signals to enrich their predictive models and advanced analytics, but sometimes it is difficult to find the most relevant data enrichment signals among the variety and volume of external data. In order to help organizations identify the data signals with the highest impact on their analysis and predictions, we built a Data Enrichment Recommender System. This article explains how the recommender system was designed and built.