job-search-backstories
A collection of backstories for job search scenarios.
Science Score: 31.0%
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✓CITATION.cff file
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○codemeta.json file
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○.zenodo.json file
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✓DOI references
Found 3 DOI reference(s) in README -
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Low similarity (7.9%) to scientific vocabulary
Repository
A collection of backstories for job search scenarios.
Basic Info
- Host: GitHub
- Owner: manuelsteiner
- License: mit
- Default Branch: main
- Size: 1.12 MB
Statistics
- Stars: 1
- Watchers: 2
- Forks: 2
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
job-search-backstories
A collection of backstories for job search scenarios.
Overview
This repository contains the backstory collection resource accompanying the following paper. For a description of how to crowdsource backstories for complex task-based search, as well as the reasoning behind it, please refer to the paper.
Manuel Steiner, Damiano Spina, Falk Scholer, Lawrence Cavedon (2021). Crowdsourcing Backstories for Complex Task-Based Search. Proceedings of the 25th Australasian Document Computing Symposium. https://doi.org/10.1145/3503516.3503526
To cite the paper, the following BibTeX entry can be used.
BibTeX
@inproceedings{steiner2021crowdsourcing,
address = {New York, NY, USA},
author = {Steiner, Manuel and Spina, Damiano and Scholer, Falk and Cavedon, Lawrence},
booktitle = {Proceedings of the 25th Australasian Document Computing Symposium},
doi = {10.1145/3503516.3503526},
location = {Virtual Event, Victoria, Australia},
publisher = {Association for Computing Machinery},
series = {ADCS '21},
title = {Crowdsourcing Backstories for Complex Task-Based Search},
year = {2021}
}
Files
The following files are provided in this repository.
job-search-backstories.csv
A collection of job search queries, links to associated job advertisements and backstories written for the particular query and job ad combination. The file contains the following data on each line, separated by comas. Strings may be enclosed by double quotes if they contain comas.
queryrefers to a job search query submitted to the job search platformjob_urlcontains the URL to the job advertisement associated with the search query in the crowdsourcing experiment. Note: The URL is publicly accessible information. Hoewever, the job advertisements themselves are expired and not publicly accessible. Thus, no sensitive or proprietary information is revealed. This content is part of the data for reproducibility reasons.backstorycontains a backstory that was written by a crowdsourcing worker for the combination of query and job advertisement.
paper.pdf
The paper as published in the ADCS proceedings.
presentation.pdf
The presentation slides as presented during the ADCS semninar series.
Acknowledgments
This research was partially supported by Australian Research Council Project LP150100252 and SEEK Ltd.
Owner
- Name: Manuel Steiner
- Login: manuelsteiner
- Kind: user
- Location: Melbourne
- Website: https://linkto.ms
- Twitter: manuelsteiner_
- Repositories: 2
- Profile: https://github.com/manuelsteiner
Citation (CITATION.bib)
@inproceedings{steiner2021crowdsourcing,
abstract = {Backstories provide vital contextual information for information
retrieval evaluation.
They are useful as textual representations of information needs, for
example to aid in relevance judgements as part of test collections for
performance evaluation, for studying longer search queries, and for
interactive retrieval.
While backstories exist for some popular search tasks and domains thanks
to evaluation campaigns such as TREC, NTCIR, and CLEF, they are
not available for a large range of other tasks and domains.
In this paper, we explore crowdsourcing as an approach for obtaining
high-quality backstories, with the aim of supporting the development of
backstories as key resources for new domains and search tasks.
Compared to typical crowdsourcing tasks in the IR domain, such as gathering
relevance judgements or short textual search queries, obtaining
backstories is more complex. Workers are required to
think of information need scenarios and put these thoughts into
comparatively lengthy text fragments. This possibly entails a higher
cognitive load and longer working time.
We describe a crowdsourcing methodology to maximise the usefulness of results,
using the creation of backstories for the job search domain as an example.
We also present and release a collection of $756$ job search backstories, which
was obtained via the proposed methodology.},
address = {New York, NY, USA},
author = {Steiner, Manuel and Spina, Damiano and Scholer, Falk and Cavedon, Lawrence},
booktitle = {Proceedings of the 25th Australasian Document Computing Symposium},
doi = {10.1145/3503516.3503526},
location = {Virtual Event, Victoria, Australia},
publisher = {Association for Computing Machinery},
series = {ADCS '21},
title = {Crowdsourcing Backstories for Complex Task-Based Search},
year = {2021}
}