System-oriented IR evaluations are limited to rather abstract understandings of real user behavior

Overview

Validating Simulations of User Query Variants

This repository contains the scripts of the experiments and evaluations, simulated queries, as well as the figures of:

Timo Breuer, Norbert Fuhr, and Philipp Schaer. 2022. Validating Simulations of User Query Variants. In Proceedings of the 44th European Conference on IR Research, ECIR 2022.

System-oriented IR evaluations are limited to rather abstract understandings of real user behavior. As a solution, simulating user interactions provides a cost-efficient way to support system-oriented experiments with more realistic directives when no interaction logs are available. While there are several user models for simulated clicks or result list interactions, very few attempts have been made towards query simulations, and it has not been investigated if these can reproduce properties of real queries. In this work, we validate simulated user query variants with the help of TREC test collections in reference to real user queries that were made for the corresponding topics. Besides, we introduce a simple yet effective method that gives better reproductions of real queries than the established methods. Our evaluation framework validates the simulations regarding the retrieval performance, reproducibility of topic score distributions, shared task utility, effort and effect, and query term similarity when compared with real user query variants. While the retrieval effectiveness and statistical properties of the topic score distributions as well as economic aspects are close to that of real queries, it is still challenging to simulate exact term matches and later query reformulations.

Directory overview

Directory Description
config/ Contains configuration files for the query simulations, experiments, and evaluations.
data/ Contains (intermediate) output data of the simulations and experiments as well as the figures of the paper.
eval/ Contains scripts of the experiments and evaluations.
sim/ Contains scripts of the query simulations.

Setup

  1. Install Anserini and index Core17 (The New York Times Annotated Corpus) according to the regression guide:
anserini/target/appassembler/bin/IndexCollection \
    -collection NewYorkTimesCollection \
    -input /path/to/core17/ \
    -index anserini/indexes/lucene-index.core17 \
    -generator DefaultLuceneDocumentGenerator \
    -threads 4 \
    -storePositions \
    -storeDocvectors \
    -storeRaw \
    -storeContents \
    > anserini/logs/log.core17 &
  1. Install the required Python packages:
pip install -r requirements.txt

Query simulation

In order to prepare the language models and simulate the queries, the scripts have to executed in the order shown in the following table. All of the outputs can be found in the data/ directory. For the sake of better code readability the names of the query reformulation strategies have been mapped: S1S1; S2S2; S2'S3; S3S4; S3'S5; S4S6; S4'S7; S4''S8. The names of the scripts and output files comply with this name mapping.

Script Description Output files
sim/make_background.py Make the background language model form all index terms of Core17. The background model is required for Controlled Query Generation (CQG) by Jordan et al. data/lm/background.csv
sim/make_cqg.py Make the CQG language models with different parameters of lambda from 0.0 to 1.0. data/lm/cqg.json
sim/simulate_queries_s12345.py Simulate TTS and KIS queries with strategies S1 to S3' data/queries/s12345.csv
sim/simulate_queries_s678.py Simulate TTS and KIS queries with strategies S4 to S4'' data/queries/s678.csv

Experimental evaluation and results

In order to reproduce the experiments of the study, the scripts have to executed in the order shown in the following table.

Script Description Output files Reproduction of ...
eval/arp.py, eval/arp_first.py, eval/arp_max.py Retrieval performance: Evaluate the Average Retrieval Performance (ARP). data/experimental_results/arp.csv, data/experimental_results/arp_first.csv, data/experimental_results/arp_max.csv Tab. A.1
eval/rmse_s12345.py, eval/rmse_s678.py Retrieval performance: Evaluate the Root-Mean-Square-Error (RMSE). data/experimental_results/rmse_map.csv, data/experimental_results/rmse_ndcg.csv, data/experimental_results/rmse_p1000.csv, data/experimental_results/rmse_uqv_vs_s12345_kis_ndcg.csv, data/experimental_results/rmse_uqv_vs_s12345_tts_ndcg.csv, data/figures/rmse_map.pdf, data/figures/rmse_ndcg.pdf, data/figures/rmse_p1000.pdf, data/figures/rmse_uqv_vs_s12345_kis_ndcg.pdf, data/figures/rmse_uqv_vs_s12345_tts_ndcg.pdf Fig. A.1, Fig. 1
eval/t-test.py Retrieval performance: Evaluate the p-values of paired t-tests. data/experimental_results/ttest.csv, data/figures/ttest.pdf Fig. A.2
eval/system_orderings.py Shared task utility: Evaluate Kendall's tau between relative system orderings. data/experimental_results/system_orderings.csv, data/figures/system_orderings.pdf Fig. 2 (left)
eval/sdcg.py Effort and effect: Evaluate the Session Discounted Cumulative Gain (sDCG). data/experimental_results/sdcg_3queries.csv, data/experimental_results/sdcg_5queries.csv, data/experimental_results/sdcg_10queries.csv, data/figures/sdcg_3queries.pdf, data/figures/sdcg_5queries.pdf, data/figures/sdcg_10queries.pdf Fig. 3 (top)
eval/economic.py Effort and effect: Evaluate tradeoffs between number of queries and browsing depth by isoquants. data/experimental_results/economic0.3.csv, data/experimental_results/economic0.4.csv, data/experimental_results/economic0.5.csv, data/figures/economic0.3.pdf, data/figures/economic0.4.pdf, data/figures/economic0.5.pdf Fig. 3 (bottom)
eval/jaccard_similarity.py Query term similarity: Evaluate query term similarities. data/experimental_results/jacc.csv, data/figures/jacc.pdf Fig. 2 (right)
Owner
IR Group at Technische Hochschule Köln
IR Group at Technische Hochschule Köln
Channel Pruning for Accelerating Very Deep Neural Networks (ICCV'17)

Channel Pruning for Accelerating Very Deep Neural Networks (ICCV'17)

Yihui He 1k Jan 03, 2023
[NeurIPS'20] Self-supervised Co-Training for Video Representation Learning. Tengda Han, Weidi Xie, Andrew Zisserman.

CoCLR: Self-supervised Co-Training for Video Representation Learning This repository contains the implementation of: InfoNCE (MoCo on videos) UberNCE

Tengda Han 271 Jan 02, 2023
PyTorch implementation of SIFT descriptor

This is an differentiable pytorch implementation of SIFT patch descriptor. It is very slow for describing one patch, but quite fast for batch. It can

Dmytro Mishkin 150 Dec 24, 2022
KAPAO is an efficient multi-person human pose estimation model that detects keypoints and poses as objects and fuses the detections to predict human poses.

KAPAO (Keypoints and Poses as Objects) KAPAO is an efficient single-stage multi-person human pose estimation model that models keypoints and poses as

Will McNally 664 Dec 30, 2022
Learning to Initialize Neural Networks for Stable and Efficient Training

GradInit This repository hosts the code for experiments in the paper, GradInit: Learning to Initialize Neural Networks for Stable and Efficient Traini

Chen Zhu 124 Dec 30, 2022
DropNAS: Grouped Operation Dropout for Differentiable Architecture Search

DropNAS: Grouped Operation Dropout for Differentiable Architecture Search DropNAS, a grouped operation dropout method for one-level DARTS, with better

weijunhong 4 Aug 15, 2022
This is our ARTS test set, an enriched test set to probe Aspect Robustness of ABSA.

This is the repository for our 2020 paper "Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis". Data We provide

35 Nov 16, 2022
Python wrapper to access the amazon selling partner API

PYTHON-AMAZON-SP-API Amazon Selling-Partner API If you have questions, please join on slack Contributions very welcome! Installation pip install pytho

Michael Primke 330 Jan 06, 2023
Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation

Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation

Bae, Gwangbin 95 Jan 04, 2023
The world's simplest facial recognition api for Python and the command line

Face Recognition You can also read a translated version of this file in Chinese 简体中文版 or in Korean 한국어 or in Japanese 日本語. Recognize and manipulate fa

Adam Geitgey 46.9k Jan 03, 2023
Occlusion robust 3D face reconstruction model in CFR-GAN (WACV 2022)

Occlusion Robust 3D face Reconstruction Yeong-Joon Ju, Gun-Hee Lee, Jung-Ho Hong, and Seong-Whan Lee Code for Occlusion Robust 3D Face Reconstruction

Yeongjoon 31 Dec 19, 2022
4K videos with annotated masks in our ICCV2021 paper 'Internal Video Inpainting by Implicit Long-range Propagation'.

Annotated 4K Videos paper | project website | code | demo video 4K videos with annotated object masks in our ICCV2021 paper: Internal Video Inpainting

Tengfei Wang 21 Nov 05, 2022
Proposed n-stage Latent Dirichlet Allocation method - A Novel Approach for LDA

n-stage Latent Dirichlet Allocation (n-LDA) Proposed n-LDA & A Novel Approach for classical LDA Latent Dirichlet Allocation (LDA) is a generative prob

Anıl Güven 4 Mar 07, 2022
Online Multi-Granularity Distillation for GAN Compression (ICCV2021)

Online Multi-Granularity Distillation for GAN Compression (ICCV2021) This repository contains the pytorch codes and trained models described in the IC

Bytedance Inc. 299 Dec 16, 2022
Elevation Mapping on GPU.

Elevation Mapping cupy Overview This is a ros package of elevation mapping on GPU. Code are written in python and uses cupy for GPU calculation. * pla

Robotic Systems Lab - Legged Robotics at ETH Zürich 183 Dec 19, 2022
The 2nd Version Of Slothybot

SlothyBot Go to this website: "https://bitly.com/SlothyBot" The 2nd Version Of Slothybot. The Bot Has Many Features, Such As: Moderation Commands; Kic

Slothy 0 Jun 01, 2022
CondenseNet: Light weighted CNN for mobile devices

CondenseNets This repository contains the code (in PyTorch) for "CondenseNet: An Efficient DenseNet using Learned Group Convolutions" paper by Gao Hua

Shichen Liu 690 Nov 30, 2022
Fast SHAP value computation for interpreting tree-based models

FastTreeSHAP FastTreeSHAP package is built based on the paper Fast TreeSHAP: Accelerating SHAP Value Computation for Trees published in NeurIPS 2021 X

LinkedIn 369 Jan 04, 2023
neural image generation

pixray Pixray is an image generation system. It combines previous ideas including: Perception Engines which uses image augmentation and iteratively op

dribnet 398 Dec 17, 2022
A Python framework for developing parallelized Computational Fluid Dynamics software to solve the hyperbolic 2D Euler equations on distributed, multi-block structured grids.

pyHype: Computational Fluid Dynamics in Python pyHype is a Python framework for developing parallelized Computational Fluid Dynamics software to solve

Mohamed Khalil 21 Nov 22, 2022