8 Signals-boosting models
This chapter covers
- Aggregating user signals to create popularity-based ranking model
- Normalizing signals for noisy query input
- Fighting signal spam in crowdsourced signals
- Applying time decays to prioritize recent signals
- Blending multiple signal types together into one model
- Choosing query-time versus index-time boosting
In chapter 4, we covered three different categories of reflected intelligence: signals boosting (popularized relevance), collaborative filtering (personalized relevance), and learning to rank (generalized relevance). In this chapter, we’ll dive deeper into the first of these, implementing signals boosting to enhance the relevance ranking of your most popular queries and documents.
In most search engines, a relatively small number of queries tend to make up a large portion of the total query volume. These popular queries, called head queries, also tend to lead to more signals (such as clicks and purchases in an e-commerce use case), which enable stronger inferences about the popularity of top search results.
Signals-boosting models directly harness these stronger inferences and are the key to ensuring your most important and highest-visibility queries are best tuned to return the most relevant documents.