Chapter 4. Making recommendations

This chapter covers

  • User-based recommenders, in depth
  • Similarity metrics
  • Item-based and other recommenders

Having spent the last chapter discussing how to evaluate recommenders and represent the data input to a recommender, it’s now time to examine the recommenders themselves in detail. That’s where the real action begins.

Previous chapters alluded to two well-known styles of recommender algorithms, both of which are implemented in Mahout: user-based recommenders and item-based recommenders. In fact, you already encountered a user-based recommender in chapter 2. This chapter explores the theory behind these algorithms, as well as the Mahout implementations of both, in detail.

Both algorithms rely on ...

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