I am sure this is something you have experienced as well: while shopping for a cellphone on Amazon, you are also shown some product recommendations of mobile accessories, such as screen guards and phone cases. Not very surprisingly, most of us end up buying one or more of these recommendations! The primary purpose of a recommendation engine in an e-commerce site is to lure buyers into purchasing more from vendors. Of course, this is no different from a salesperson trying to up-sell or cross-sell to customers in a physical store.

You may recollect the Customers Who Bought This Item Also Bought This heading on Amazon (or any e-commerce site) where recommendations are shown. The aim of these recommendations is to get you to buy not just one product but a product combo, therefore pushing the sales revenues in an upward direction. Recommendations on Amazon are so successful that McKinsey estimated that a whopping 35% of the overall sales made on Amazon is due to their recommendations!

In this chapter, we will learn about the theory and implementation of a recommendation engine to suggest jokes to users. To do this, we use the Jester's jokes dataset that is available in the recommenderlab library of R. We will cover the following major topics:

• Fundamental aspects of recommendation engines
• Understanding the Jokes recommendation problem and the dataset
• Recommendation system using an item-based collaborative filtering technique
• Recommendation system using a user-based collaborative filtering technique
• Recommendation system using an association-rule mining technique
• Content-based recommendation engine
• Hybrid recommendation system for Jokes recommendation