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A Deep Learning Approach for Selective Relevance Feedback
Additionally, to further improve retrieval effectiveness with this selective PRF approach, we make use of the model's confidence estimates to combine the information from the original and expanded queries.
On the Feasibility and Robustness of Pointwise Evaluation of Query Performance Prediction
Despite the retrieval effectiveness of queries being mutually independent of one another, the evaluation of query performance prediction (QPP) systems has been carried out by measuring rank correlation over an entire set of queries.
Deep-QPP: A Pairwise Interaction-based Deep Learning Model for Supervised Query Performance Prediction
In contrast to unsupervised approaches that rely on various statistics of document score distributions, our approach is entirely data-driven.
An Analysis of Variations in the Effectiveness of Query Performance Prediction
An important characteristic of QPP evaluation is that, since the ground truth retrieval effectiveness for QPP evaluation can be measured with different metrics, the ground truth itself is not absolute, which is in contrast to other retrieval tasks, such as that of ad-hoc retrieval.
