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
TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters.

TensorFlowOnSpark TensorFlowOnSpark brings scalable deep learning to Apache Hadoop and Apache Spark clusters. By combining salient features from the T

Yahoo 3.8k Jan 04, 2023
DNA-RECON { Automatic Web Reconnaissance Tool }

ABOUT TOOL : DNA-RECON is an automatic web reconnaissance tool written in python. This tool made for reconnaissance and information gathering with an

NIKUNJ BHATT 25 Aug 11, 2021
Code release for NeuS

NeuS We present a novel neural surface reconstruction method, called NeuS, for reconstructing objects and scenes with high fidelity from 2D image inpu

Peng Wang 813 Jan 04, 2023
Official implementation of "Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision" ECCV2020

XDVioDet Official implementation of "Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision" ECCV2020. The proj

peng 64 Dec 12, 2022
This repository is an official implementation of the paper MOTR: End-to-End Multiple-Object Tracking with TRansformer.

MOTR: End-to-End Multiple-Object Tracking with TRansformer This repository is an official implementation of the paper MOTR: End-to-End Multiple-Object

348 Jan 07, 2023
DPC: Unsupervised Deep Point Correspondence via Cross and Self Construction (3DV 2021)

DPC: Unsupervised Deep Point Correspondence via Cross and Self Construction (3DV 2021) This repo is the implementation of DPC. Tested environment Pyth

Dvir Ginzburg 30 Nov 30, 2022
App customer segmentation cohort rfm clustering

CUSTOMER SEGMENTATION COHORT RFM CLUSTERING TỔNG QUAN VỀ HỆ THỐNG DỮ LIỆU Nên chuyển qua theme màu dark thì sẽ nhìn đẹp hơn https://customer-segmentat

hieulmsc 3 Dec 18, 2021
Code release for the ICML 2021 paper "PixelTransformer: Sample Conditioned Signal Generation".

PixelTransformer Code release for the ICML 2021 paper "PixelTransformer: Sample Conditioned Signal Generation". Project Page Installation Please insta

Shubham Tulsiani 24 Dec 17, 2022
A new play-and-plug method of controlling an existing generative model with conditioning attributes and their compositions.

Viz-It Data Visualizer Web-Application If I ask you where most of the data wrangler looses their time ? It is Data Overview and EDA. Presenting "Viz-I

NVIDIA Research Projects 66 Jan 01, 2023
QAT(quantize aware training) for classification with MQBench

MQBench Quantization Aware Training with PyTorch I am using MQBench(Model Quantization Benchmark)(http://mqbench.tech/) to quantize the model for depl

Ling Zhang 29 Nov 18, 2022
OpenMMLab Detection Toolbox and Benchmark

MMDetection is an open source object detection toolbox based on PyTorch. It is a part of the OpenMMLab project.

OpenMMLab 22.5k Jan 05, 2023
A Rao-Blackwellized Particle Filter for 6D Object Pose Tracking

PoseRBPF: A Rao-Blackwellized Particle Filter for 6D Object Pose Tracking PoseRBPF Paper Self-supervision Paper Pose Estimation Video Robot Manipulati

NVIDIA Research Projects 107 Dec 25, 2022
Transfer-Learn is an open-source and well-documented library for Transfer Learning.

Transfer-Learn is an open-source and well-documented library for Transfer Learning. It is based on pure PyTorch with high performance and friendly API. Our code is pythonic, and the design is consist

THUML @ Tsinghua University 2.2k Jan 03, 2023
A Demo server serving Bert through ONNX with GPU written in Rust with <3

Demo BERT ONNX server written in rust This demo showcase the use of onnxruntime-rs on BERT with a GPU on CUDA 11 served by actix-web and tokenized wit

Xavier Tao 28 Jan 01, 2023
Turn based roguelike in python

pyTB Turn based roguelike in python Documentation can be found here: http://mcgillij.github.io/pyTB/index.html Screenshot Dependencies Written in Pyth

Jason McGillivray 4 Sep 29, 2022
Distributing reference energies for SMIRNOFF implementations

Warning: This code is currently experimental and under active development. Is it not yet suitable for distribution or use as reference implementation.

Open Force Field Initiative 1 Dec 07, 2021
Python implementation of "Single Image Haze Removal Using Dark Channel Prior"

##Dependencies pillow(~2.6.0) Numpy(~1.9.0) If the scripts throw AttributeError: __float__, make sure your pillow has jpeg support e.g. try: $ sudo ap

Joyee Cheung 73 Dec 20, 2022
Accommodating supervised learning algorithms for the historical prices of the world's favorite cryptocurrency and boosting it through LightGBM.

Accommodating supervised learning algorithms for the historical prices of the world's favorite cryptocurrency and boosting it through LightGBM.

1 Nov 27, 2021
HyperLib: Deep learning in the Hyperbolic space

HyperLib: Deep learning in the Hyperbolic space Background This library implements common Neural Network components in the hypberbolic space (using th

105 Dec 25, 2022
Code for ECIR'20 paper Diagnosing BERT with Retrieval Heuristics

Bert Axioms This is the repository with the code for the Paper Diagnosing BERT with Retrieval Heuristics Required Data In order to run this code, you

Arthur Câmara 5 Jan 21, 2022