This is the code repository for the PRE model - Precision-Recall-Effort query optimization framework.
Description: Precision-Recall-Effort query framework is a query formulation and reformulation using Precision-Recall-Effort framework by maximizing the estimated recall and precision of the retrieval results and minimizing the effort for making the query.
Labhishetty et al., In Proceedings of the 2022 ACM SIGIR ICTIR, PRE: A Precision-Recall-Effort Optimization Framework for Query Simulation, https://dl.acm.org/doi/10.1145/3539813.3545136
Code/ : to simulate queries for TREC session track tasks and to simulate session through query reformulations using PRE, to evaluate simulated queries/sessions using TREC user queries and sessions.
Code_CCQF_model/ : to simulate queries using cognitive and communication query formulation framework - first version of PRE, optimizing precision-recall together and minimizing effort.
figures/ : sensitivity of effort, recall and precision parameters.
recall_precision_hists/ : Example histograms of recall and precision scores for generated queries of topics.
If you use our work in your research please cite:
@inproceedings{10.1145/3539813.3545136,
author = {Labhishetty, Sahiti and Zhai, ChengXiang},
title = {PRE: A Precision-Recall-Effort Optimization Framework for Query Simulation},
year = {2022},
isbn = {9781450394123},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3539813.3545136},
doi = {10.1145/3539813.3545136},
booktitle = {Proceedings of the 2022 ACM SIGIR International Conference on Theory of Information Retrieval},
pages = {51–60},
numpages = {10},
keywords = {knowledge state, query simulation, formal interpretable framework},
location = {Madrid, Spain},
series = {ICTIR '22}
}
By using this source code you agree to the license described in https://github.com/sahitilucky/Precision_recall_effort_query_formulation_model/blob/master/LICENSE.md.