Predicting Reputation Score of Users in Stack-overflow with Alternate Data

Sahil Yerawar, Sagar Jinde, P. K. Srijith, Maunendra Sankar Desarkar, K. M. Annervaz, Shubhashis Sengupta

2022

Abstract

The community question and answering (CQA) sites such as Stack Overflow are used by many users around the world to obtain answers to technical questions. Here, the reliability of a user is determined using metrics such as reputation score. It is important for the CQA sites to assess the reputation score of the new users joining the site. Accurate estimation of reputation scores of these cold start users can help in tasks like question routing, expert recommendation and ranking etc. However, lack of activity information makes it quite difficult to assess the reputation score for new users. We propose an approach which makes use of alternate data associated with the users to predict the reputation score of the new users. We show that the alternate data obtained using users’ personal websites could improve the reputation score performance. We develop deep learning models based on feature distillation, such as the student-teacher models, to improve the reputation score prediction of new users from the alternate data. We demonstrate the effectiveness of the proposed approaches on the publicly available stack overflow data and publicly available alternate data.

Paper Citation

in Harvard Style

Yerawar S., Jinde S., Srijith P., Desarkar M., Annervaz K. and Sengupta S. (2022). Predicting Reputation Score of Users in Stack-overflow with Alternate Data. In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR; ISBN 978-989-758-614-9, SciTePress, pages 355-362. DOI: 10.5220/0011591900003335

in Bibtex Style

@conference{kdir22,
author={Sahil Yerawar and Sagar Jinde and P. K. Srijith and Maunendra Sankar Desarkar and K. M. Annervaz and Shubhashis Sengupta},
title={Predicting Reputation Score of Users in Stack-overflow with Alternate Data},
booktitle={Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR},
year={2022},
pages={355-362},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011591900003335},
isbn={978-989-758-614-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR
TI - Predicting Reputation Score of Users in Stack-overflow with Alternate Data
SN - 978-989-758-614-9
AU - Yerawar S.
AU - Jinde S.
AU - Srijith P.
AU - Desarkar M.
AU - Annervaz K.
AU - Sengupta S.
PY - 2022
SP - 355
EP - 362
DO - 10.5220/0011591900003335
PB - SciTePress