A Lexicon-based Collaborative Filtering Approach for Recommendation Systems

Mara Deac-Petruşel

2022

Abstract

Users purchasing items from e-commerce websites are expressing their satisfaction and sentiment about their acquisition using text-based reviews and numerical ratings. Traditional collaborative filtering techniques are entirely dependent on the users’ scalar ratings, which are lacking any semantic explanation of the users’ preferences. This approach was designed to explore the text-based item evaluation using a Sentiment Analysis Lexicon. The proposed lexicon-based k nearest neighbors collaborative filtering technique replaces the numerical rating with a computed sentiment rating in the neighborhood determination step. The conducted experiments reveal that the resulting text-based recommendation system produces reliable values in terms of mean absolute error and root mean square error and accurate recommendations for users.

Paper Citation

in Harvard Style

Deac-Petruşel M. (2022). A Lexicon-based Collaborative Filtering Approach for Recommendation Systems. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 203-210. DOI: 10.5220/0010801200003116

in Bibtex Style

@conference{icaart22,
author={Mara Deac-Petruşel},
title={A Lexicon-based Collaborative Filtering Approach for Recommendation Systems},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={203-210},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010801200003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - A Lexicon-based Collaborative Filtering Approach for Recommendation Systems
SN - 978-989-758-547-0
AU - Deac-Petruşel M.
PY - 2022
SP - 203
EP - 210
DO - 10.5220/0010801200003116