Back to the roots: A probabilistic framework for query-performance prediction

The query-performance prediction task is estimating the effectiveness of a search performed in response to a query when no relevance judgments are available. Although there exist many effective prediction methods, these differ substantially in their basic principles, and rely on diverse hypotheses about the characteristics of effective retrieval. We present a novel fundamental probabilistic prediction framework. Using the framework, we derive and explain various previously proposed prediction methods that might seem completely different, but turn out to share the same formal basis. The derivations provide new perspectives on several predictors (e.g., Clarity). The framework is also used to devise new prediction approaches that outperform the state-of-the-art.