Genetic algorithm-based matrix factorization for missing value prediction

Abstract

Sparsity is a major problem in the areas like data mining and pattern recognition. In recommender systems, predictions based on these few observations lead to avoidance of inherent latent features of the user corresponding to the item. Similarly, in different crowdsourcing based opinion aggregation models, there is a minimal chance to obtain opinions from all the crowd workers. Even this sparsity problem has an extensive effect in predicting actual rating of a particular item due to limited and incomplete observations. To deal with this issue, in this article, a genetic algorithm based matrix factorization technique is proposed to estimate the missing entries in the response matrix that contains workers’ responses over some questions. We have created three synthetic datasets and used one real-life dataset to show the efficacy of the proposed method over the other state-of-the-art approaches.