Summary

In this chapter, we have discussed product recommender systems. We have learned how personalized product recommendations improve conversion and customer retention rates, according to a study conducted by Salesforce. We have discussed the two approaches, collaborative filtering and content-based filtering, to building product recommendation systems; how they differ from one another; and what their assumptions are. Then, we dove deeper into how we can build collaborative filtering-based recommender systems. As you might recall, the first step to building a collaborative filtering-based recommender system is to build a user-to-item matrix, and then the next step is to use cosine similarity to compute the similarities between the users. We have also discussed the two different approaches to utilizing a collaborative filtering algorithm for product recommendations—a...