Chapter 13. Ranking and learning to rank
This book is all about learning, and in this chapter, you’ll learn how to rank.
- You’ll reformulate the recommender problem to a ranking problem.
- You’ll look at Foursquare’s ranking method and how it uses multiple sources.
- You’ll go through the different types of Learning to Rank (LTR) algorithms and learn how to distinguish pointwise, pairwise, and listwise comparisons of ranks.
- You’ll learn about the Bayesian Personalized Ranking (BPR) algorithm, which is a promising algorithm to implement.
Are all these chapters on recommender algorithms starting to look the same? If so, you’re in luck, because now you’re going to start something completely different. Instead of focusing on recommendations as a rating ...
Get Practical Recommender Systems now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
