Estimation Methods for Ranking and Recommendation: Addressing Bias, Variance, Consistency, and Efficiency

Evaluation and optimization of ranking and recommendation systems involves estimating various quantities such as relevance, gradients or probabilities. Importantly, different estimation procedures may vary in terms of their statistical and computational properties, which can in turn significantly affect the downstream performance for a given task. In this thesis, we study ranking and recommendation estimation methods with a focus on some of their key properties: bias, variance, consistency and efficiency.

In its entirety, this thesis demonstrates that statistical and computational properties of estimators play a significant role in learning from user interactions.
It highlights their importance across a wide range of settings and provides actionable guidance for developing estimation methods for ranking and recommendation that address bias, variance, consistency and efficiency.