We started the chapter with an overview of recommender systems. We introduced our retail case and the association rule mining algorithm. Then we applied association rule mining to design a cross-selling campaign. We went on to understand weighted association rule mining and its applications. Following that, we introduced the HITS algorithm and its use in transaction data. Next, we studied the negative association rules discovery process and its use. We showed you different ways to visualize association rules. Finally, we created a small web application using R Shiny to demonstrate some of the concepts we learned.
In the next chapter, we will look at another recommendation system algorithm called content based filtering. We will see how this method can help address the famous cold start problem in recommendation systems. Furthermore, we will introduce the concept of fuzzy ranking to order the final recommendations.
