This repository hosts code and data associated with the paper
@inproceedings{striebel2023career,
author = {Striebel, Jacob and Myers, Rebecca and Liu, Xiaozhong},
year = 2023,
title = {Career-Based Explainable Course Recommendation},
booktitle = {Proceedings of i{C}onference 2023}
}
The results of the system evaluation, which are reported in the paper, are given in the file
eval/out/eval_table.csv
The hand-scored outputs of our system and the two baselines, from which the above results are tabulated, are given in the file
eval/in/blind_eval_filled.csv
To rerun the tabulation script and/or our full system and the baselines, first create a Python virtual environment and install the required packages by executing the following Linux shell commands from this repository's root directory:1
python -m venv myvenv
source myvenv/bin/activate
pip install -r requirements.txt
To retabulate the evaluation results (eval/out/eval_table.csv
) from the
hand-scored system outputs (eval/in/blind_eval_filled.csv
), execute
python src/tabulate_evaluation_forms.py
To rerun our explainable course recommendation system and the two baselines using the job queries given in the paper, execute
python src/generate_evaluation_forms.py
This will generate the following two files
eval/out/full_eval.csv
eval/out/blind_eval.csv
To perform your own evaluation, copy the first file to
eval/in/full_eval.csv
and copy the second file to
eval/in/blind_eval_filled.csv
Open blind_eval_filled.csv
with a spreadsheet program, and enter a score for
each course recommendation and each explanation with respect to their job query.
Use a four-point scale with 0 worst and 3 best.
After entering the scores, save the file.
Then run
python src/tabulate_evaluation_forms.py
The file
eval/out/eval_table.csv
will be generated which contains the results of the new evaluation.
Footnotes
-
We tested the workflow described in this document on Fedora 33 (Linux) with Python 3.9.9. ↩