Different business needs—from suggesting related products after buying your new laptop, to compiling the perfect driving playlist, to helping you reconnect with long lost schoolmates—led to the development of different recommendation algorithms. A key part of rolling out a recommender system is picking the right approach for the problem at hand to fully take advantage of the data available. We'll take a look at the most common and most successful algorithms.
The simplest types of recommendations, from a technical and algorithmic perspective, are the non-personalized ones. That is, they are not customized to take into account specific user preferences. Such recommendations can include best-selling products, various top 10 songs, blockbuster movies, or the most downloaded apps of the week.
Non-personalized recommendations are less challenging technically, but also considerably less powerful...