Open AccessComparative Analysis of Collaborative Filtering-Based Predictors of Scores in Surveys of a Large Company
Author(s) -
Markos F. B. G. Oliveira,
Myriam Regattieri Delgado,
Ricardo Lüders
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/eniac.2021.18299
Subject(s) - collaborative filtering , computer science , context (archaeology) , matrix decomposition , process (computing) , quality (philosophy) , machine learning , recommender system , artificial intelligence , data mining , paleontology , biology , philosophy , eigenvalues and eigenvectors , physics , epistemology , quantum mechanics , operating system
Collaborative Filtering (CF) can be understood as the process of predicting the preferences of users and deriving useful patterns by studying their activities. In the survey context, it can be used to predict answers to questions as combinations of other available answers. In this paper, we aim to test five CF-based algorithms (item-item, iterative matrix factorization, neural collaborative filtering, logistic matrix factorization, and an ensemble of them) to estimate scores in four survey applications (checkpoints) composed of 700,000 employee's ratings. These data have been collected from 2019 to 2020 by a large Brazilian tech company with more than 10,000 employees. The results show that collaborative filtering approaches provide relevant alternatives to score questions of surveys. They provided good quality estimates. This result can be further explored to eventually reduce the size of questionnaires, avoiding burden phenomena faced by respondents when dealing with large surveys.