Discordance Minimization-based Imputation Algorithms for Missing Values in Rating Data
This addresses missing data issues in rating-based applications like education and healthcare, but it is incremental as it builds on existing imputation methods with a new optimization approach.
The paper tackles the problem of missing values in combined rating lists by proposing imputation algorithms that minimize total rating discordance, and computational experiments show these methods outperform state-of-the-art general imputation methods in accuracy.
Ratings are frequently used to evaluate and compare subjects in various applications, from education to healthcare, because ratings provide succinct yet credible measures for comparing subjects. However, when multiple rating lists are combined or considered together, subjects often have missing ratings, because most rating lists do not rate every subject in the combined list. In this study, we propose analyses on missing value patterns using six real-world data sets in various applications, as well as the conditions for applicability of imputation algorithms. Based on the special structures and properties derived from the analyses, we propose optimization models and algorithms that minimize the total rating discordance across rating providers to impute missing ratings in the combined rating lists, using only the known rating information. The total rating discordance is defined as the sum of the pairwise discordance metric, which can be written as a quadratic function. Computational experiments based on real-world and synthetic rating data sets show that the proposed methods outperform the state-of-the-art general imputation methods in the literature in terms of imputation accuracy.