10 Learning to rank for generalizable search relevance
This chapter covers
- An introduction to machine-learned ranking, also known as learning to rank (LTR)
- How LTR differs from other machine learning methods
- Training and deploying a ranking classifier
- Feature engineering, judgment lists, and integrating machine-learned ranking models into a search engine
- Validating an LTR model using a train/test split
- Performance tradeoffs for LTR-based ranking models
It’s a random Tuesday. You review your search logs, and the searches range from the frustrated runner’s polar m430 running watch charger query to the worried hypochondriac’s weird bump on nose - cancer? to the curious cinephile’s william shatner first film. Even though these may be one-off queries, you know each user expects nothing less than amazing search results.
You feel hopeless. You know many query strings, by themselves, are distressingly rare. You have very little click data to know what’s relevant for these searches. Every day gets more challenging: trends, use cases, products, user interfaces, and even user terminology evolve. How can anyone hope to build search that amazes when users seem to constantly surprise us with new ways of searching?