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 ...