Chapter 5. Recommendation Systems

Recommendation systems find their natural application whenever a user is exposed to a wide choice of products or services that they cannot evaluate in a reasonable timeframe. These engines are an important part of an e-commerce business because they assist the clients on the web to facilitate the task of deciding the appropriate items to buy or choose over a large number of candidates not relevant to the end user. Typical examples are Amazon, Netflix, eBay, and Google Play stores that suggest each user the items they may like to buy using the historical data they have collected. Different techniques have been developed in the past 20 years and we will focus on the most important (and employed) methods used in the industry to date, specifying the advantages and disadvantages that characterize each of these methods. The recommendation systems are classified in Content-based Filtering (CBF) and Collaborative Filtering (CF) techniques and other different...