Introduction to Recommender Systems in 2018 Tryolabs Blog

Many e-commerce and retail companies are leveraging the power of data and boosting sales by implementing recommender systems on their websites. In short, these systems aim to predict users' interests and recommend items that quite likely are interesting for them. Data required for recommender systems stems from explicit user ratings after watching a movie or listening to a song, from implicit search engine queries and purchase histories, or from other knowledge about the users/items themselves. Sites like Spotify, YouTube or Netflix use that data in order to suggest playlists, so-called Daily mixes, or to make video recommendations, respectively. In this blog post, we'll describe the broad types of the most popular recommender systems and give insights into how they work, going through a few examples. To give some motivation on the subject and help decide whether it's a worthwhile investment, we'll point to some real-life case studies, talk about the high level requirements for implementing recommender systems, and discuss how they can be evaluated fairly.