Distributed Neural Networks for Missing Big Data Imputation
Alessio Petrozziello, Ivan Jordanov, Christian Sommeregger · 2018
In this paper we investigate the use of Distributed Neural Networks for the imputation of missing values in Big Data context. The presented framework for data imputation is implemented in Spark, allowing easy imputation as an additional step to the data pre-processing pipeline. The Distributed Neural Networks model is using Mini-batch Stochastic Gradient Descent, scaling well with the cluster size and minimizing the communication among the workers. The model is tested on a real-world Recommender Systems dataset, where the missing data is generally a problem for new items, as the systems ranking is usually biased towards the popular items. The model is compared with univariate (Mean and Median Imputation) and multivariate (K-Nearest Neighbours and Linear Regression) imputation techniques, and its performance is validated using prediction accuracy and speed. Furthermore, we evaluate the speedup compared to the sequential implementation of Neural Networks with Stochastic Gradient Descent.