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Practical Recommender Systems
book

Practical Recommender Systems

by Kim Falk
February 2019
Beginner to intermediate
432 pages
13h 29m
English
Manning Publications
Content preview from Practical Recommender Systems

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 ...

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Publisher Resources

ISBN: 9781617292705Publisher SupportPublisher WebsiteErrata Page