Chapter 9. Evaluating and testing your recommender

The Netflix Prize abstracted the recommendation problem to a simplified proxy of accurately predicting ratings. It is now clear that this is just one of many components in an effective industrial recommendation system. They also need to account for factors like diversity, context, evidence, freshness, and novelty.

Xavier Amatriain et al.[1]


Amatriain, Xavier et al., Past, Present, and Future of Recommender Systems: An Industry Perspective (Recsys, 2016).

After studying this chapter, you’ll gain experience in the following areas:

  • Evaluating the effectiveness of a recommender algorithm
  • Splitting data sets into training data and test data
  • Building offline experiments to evaluate recommender ...

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