BNN: Boosting Neural Network Framework Utilizing Limited Amount of Data

Abstract

Deep learning (DL) algorithms have played a major role in achieving state-of-the-art (SOTA) performance in various learning applications, including computer vision, natural language processing, and recommendation systems (RSs). However, these methods are based on a vast amount of data and do not perform as well when there is a limited amount of data available. Moreover, some of these applications (e.g., RSs) suffer from other issues such as data sparsity and the cold-start problem. While recent research on RSs used DL models based on side information (SI) (e.g., product reviews, film plots, etc.) to tackle these challenges, we propose boosting neural network (BNN), a new DL framework for capturing complex patterns, which requires just a limited amount of data. Unlike conventional boosting, BNN does not sum the predictions generated by its components. Instead, it uses these predictions as new SI features which enhances accuracy. Our framework can be utilized for many problems, including classification, regression, and ranking. In this paper, we demonstrate BNN's use for addressing a classification task. Comprehensive experiments conducted to illustrate BNN's effectiveness on three real-world datasets demonstrated its ability to outperform existing SOTA models for classification tasks (e.g., clickthrough rate prediction).