# Deep-QPP: A Pairwise Interaction-based Deep Learning Model for Supervised Query Performance Prediction

arXiv: https://arxiv.org/abs/2202.07376 · HTML: https://mlsanity.com/paper/2202.07376/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2022.

## Built on by (most cited first, 13 of 13)

- [Query Performance Prediction using Relevance Judgments Generated by Large Language Models](https://mlsanity.com/paper/2404.01012/) (2024)
- [Query Performance Prediction: From Ad-hoc to Conversational Search](https://mlsanity.com/paper/2305.10923/) (2023)
- [Is Relevance Propagated from Retriever to Generator in RAG?](https://mlsanity.com/paper/2502.15025/) (2025)
- [Performance Prediction for Multi-hop Questions](https://mlsanity.com/paper/2308.06431/) (2023)
- [One size doesn't fit all: Predicting the Number of Examples for In-Context Learning](https://mlsanity.com/paper/2403.06402/) (2024)
- [iQPP: A Benchmark for Image Query Performance Prediction](https://mlsanity.com/paper/2302.10126/) (2023)
- [PQPP: A Joint Benchmark for Text-to-Image Prompt and Query Performance Prediction](https://mlsanity.com/paper/2406.04746/) (2024)
- [Predicting Retrieval Utility and Answer Quality in Retrieval-Augmented Generation](https://mlsanity.com/paper/2601.14546/) (2026)
- [Can LLM Rerankers Predict Their Own Ranking Performance?](https://mlsanity.com/paper/2606.03535/) (2026)
- [Predicting Partial Answer Quality and Utility in Agentic Retrieval-Augmented Generation](https://mlsanity.com/paper/2609.16453/) (2026)
- [The Surprising Effectiveness of Rankers Trained on Expanded Queries](https://mlsanity.com/paper/2404.02587/) (2024)
- [VQPP: Video Query Performance Prediction Benchmark](https://mlsanity.com/paper/2602.17814/) (2026)
- [One Size Does Not Fit All! Dynamic Retriever and Generator Selection for RAG](https://mlsanity.com/paper/2609.17709/) (2026)

## Abstract (arXiv)

Motivated by the recent success of end-to-end deep neural models for ranking tasks, we present here a supervised end-to-end neural approach for query performance prediction (QPP). In contrast to unsupervised approaches that rely on various statistics of document score distributions, our approach is entirely data-driven. Further, in contrast to weakly supervised approaches, our method also does not rely on the outputs from different QPP estimators. In particular, our model leverages information from the semantic interactions between the terms of a query and those in the top-documents retrieved with it. The architecture of the model comprises multiple layers of 2D convolution filters followed by a feed-forward layer of parameters. Experiments on standard test collections demonstrate that our proposed supervised approach outperforms other state-of-the-art supervised and unsupervised approaches.