@article{ART003139224},
author={Soojung Lee},
title={Optimizing Similarity for User-based Collaborative Filtering},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2024},
volume={29},
number={11},
pages={243-250},
doi={10.9708/jksci.2024.29.11.243}
TY - JOUR
AU - Soojung Lee
TI - Optimizing Similarity for User-based Collaborative Filtering
JO - Journal of The Korea Society of Computer and Information
PY - 2024
VL - 29
IS - 11
PB - The Korean Society Of Computer And Information
SP - 243
EP - 250
SN - 1598-849X
AB - Collaborative filtering is one of the most widely known implementation methods of recommender systems, which recommends items that similar users have preferred in the past. Therefore, similarity measurement is a very important factor that determines the performance of the system. In this study, in order to solve the shortcomings of the existing single or integrated heuristic similarity measures, the genetic algorithm was used to calculate the optimal similarity between users per item genre. In addition, in order to solve the data scalability problem, the number of users for calculating similarity for each genre was limited according to a preset threshold, and the average of the ratings of the items was used to solve the data sparsity problem. Through performance experiments, the optimal probabilities of the genetic operators were obtained and the prediction accuracy performance was analyzed. As a result, it was confirmed that the performance of the proposed method was superior to the existing methods, especially in a sparse data environment.
KW - Similarity Measure;Genetic Algorithm;Data Sparsity Problem;Data Scalability Problem;Collaborative Filtering;Recommender System
DO - 10.9708/jksci.2024.29.11.243
ER -
Soojung Lee. (2024). Optimizing Similarity for User-based Collaborative Filtering. Journal of The Korea Society of Computer and Information, 29(11), 243-250.
Soojung Lee. 2024, "Optimizing Similarity for User-based Collaborative Filtering", Journal of The Korea Society of Computer and Information, vol.29, no.11 pp.243-250. Available from: doi:10.9708/jksci.2024.29.11.243
Soojung Lee "Optimizing Similarity for User-based Collaborative Filtering" Journal of The Korea Society of Computer and Information 29.11 pp.243-250 (2024) : 243.
Soojung Lee. Optimizing Similarity for User-based Collaborative Filtering. 2024; 29(11), 243-250. Available from: doi:10.9708/jksci.2024.29.11.243
Soojung Lee. "Optimizing Similarity for User-based Collaborative Filtering" Journal of The Korea Society of Computer and Information 29, no.11 (2024) : 243-250.doi: 10.9708/jksci.2024.29.11.243
Soojung Lee. Optimizing Similarity for User-based Collaborative Filtering. Journal of The Korea Society of Computer and Information, 29(11), 243-250. doi: 10.9708/jksci.2024.29.11.243
Soojung Lee. Optimizing Similarity for User-based Collaborative Filtering. Journal of The Korea Society of Computer and Information. 2024; 29(11) 243-250. doi: 10.9708/jksci.2024.29.11.243
Soojung Lee. Optimizing Similarity for User-based Collaborative Filtering. 2024; 29(11), 243-250. Available from: doi:10.9708/jksci.2024.29.11.243
Soojung Lee. "Optimizing Similarity for User-based Collaborative Filtering" Journal of The Korea Society of Computer and Information 29, no.11 (2024) : 243-250.doi: 10.9708/jksci.2024.29.11.243
