@misc{10481/77378,
year = {2022},
month = {9},
url = {https://hdl.handle.net/10481/77378},
abstract = {In group decision-making (GDM), fuzzy preference relations (FPRs) refer to pairwise preferences in
the form of a matrix. Within the field of GDM, the problem of estimating missing values is of utmost
importance, since many experts provide incomplete preferences. In this paper, we propose a new
method called the entropy-based method for estimating the missing values in the FPR. We compared
the accuracy of our algorithm for predicting the missing values with the best candidate algorithm
from state of the art achievements. In the proposed entropy-based method, we took advantage of
pairwise preferences to achieve good results by storing extra information compared to single rating
scores, for example, a pairwise comparison of alternatives vs. the alternative’s score from one to five
stars. The entropy-based method maps the prediction problem into a matrix factorization problem, and
thus the solution for the matrix factorization can be expressed in the form of latent expert features
and latent alternative features. Thus, the entropy-based method embeds alternatives and experts in
the same latent feature space. By virtue of this embedding, another novelty of our approach is to
use the similarity of experts, as well as the similarity between alternatives, to infer the missing values
even when only minimal data are available for some alternatives from some experts. Note that current
approaches may fail to provide any output in such cases. Apart from estimating missing values, another
salient contribution of this paper is to use the proposed entropy-based method to rank the alternatives.
It is worth mentioning that ranking alternatives have many possible applications in GDM, especially
in group recommendation systems (GRS).},
organization = {Andalusian Government	P20 00673
PID2019-103880RB-I00
MCIN/AEI/10.13039/501100011033},
publisher = {Elsevier},
keywords = {Estimating missing values},
keywords = {Group decision-making},
keywords = {Fuzzy preference relations},
keywords = {Ranking},
keywords = {Pairwise preferences},
keywords = {Group recommendation system},
title = {Predicting missing pairwise preferences from similarity features in group decision making},
doi = {10.1016/j.knosys.2022.109860},
author = {Abolghasemi, Roza and Herrera Viedma, Enrique},
}