How do Recommendation Engines work? And What are the Benefits?

According to the article Using Machine Learning on Compute Engine to Make Product Recommendations, a typical recommendation engine processes data through the following four phases namely collection, storing, analyzing and filtering. The first step in creating a recommendation engine is gathering data. Data can be either explicit or implicit data. Explicit data would consist of data inputted by users such as ratings and comments on products. And implicit data would be the order history/return history, Cart events, Pageviews, Click thru and search log.