Practical Recommender Systems

Chapter 7. Finding similarities among users and among content

Similarity can be calculated in many ways, and we’ll look at most of those. In this chapter

• You’ll gain an understanding of what similarity and its cousin, distance, are.
• You’ll look at how to calculate similarity between sets of items.
• With similarity functions, you’ll measure how alike two users are, using the ratings they’ve given to content.
• It sometimes helps to group users, so you’ll do that using the k-means clustering algorithm.

Chapter 6 described non-personalized recommendations and the association rules. Association rules are a way to connect content without looking at the item or the users who consumed them. Personalized recommendations, however, almost always contain ...