This chapter covers
- Using live users to get feedback on our LTR model
- A/B testing search relevance solutions with live users
- Exploring possible relevant results beyond the top results we always show users
- Balancing exploiting what we’ve learned from historical data and exploring what might be relevant
So far our Learning to Rank work has happened in the lab. In previous chapters, we built models using automatically constructed training data from user clicks. In this chapter, we’ll take our model into the real world for a test drive with (simulated) live users!
Recall that we compared the full Automated Learning to Rank system to a self-driving car. Internally, the car has an engine: the end-to-end model retraining on historical judgements as discussed in Chapter 10. In Chapter 11 we compared our model’s training data to self driving car directions: what should we optimize to automatically learn judgements based on previous interactions with search results? We built training data, and overcame key biases inherent in click data.