BNN: Boosting Neural Network Framework Utilizing Limited Amount of Data

Amit Livne, Roy Dor, Bracha Shapira, Lior Rokach

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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).

Original languageEnglish GB
Title of host publicationCIKM 2021 - Proceedings of the 30th ACM International Conference on Information and Knowledge Management
Subtitle of host publicationProceedings of the 30th ACM International Conference on Information & Knowledge Management
PublisherAssociation for Computing Machinery
Pages1150-1159
Number of pages10
ISBN (Electronic)9781450384469
DOIs
StatePublished - 26 Oct 2021
Event30th ACM International Conference on Information and Knowledge Management, CIKM 2021 - Virtual, Online, Australia
Duration: 1 Nov 20215 Nov 2021

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference30th ACM International Conference on Information and Knowledge Management, CIKM 2021
Country/TerritoryAustralia
CityVirtual, Online
Period1/11/215/11/21

Keywords

  • boosting
  • click-through rate prediction
  • deep neural network
  • recommender systems

ASJC Scopus subject areas

  • Business, Management and Accounting (all)
  • Decision Sciences (all)

Fingerprint

Dive into the research topics of 'BNN: Boosting Neural Network Framework Utilizing Limited Amount of Data'. Together they form a unique fingerprint.

Cite this