Modelling User Habits and Providing Recommendations based on the Hybrid Broadcast Broadband Television using Neural Networks


Topalli I., KILINÇ S.

IEEE TRANSACTIONS ON CONSUMER ELECTRONICS, vol.62, no.2, pp.182-190, 2016 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 62 Issue: 2
  • Publication Date: 2016
  • Doi Number: 10.1109/tce.2016.7514718
  • Journal Name: IEEE TRANSACTIONS ON CONSUMER ELECTRONICS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.182-190
  • Keywords: HbbTV, Data Collection, Neural Networks, MLP, Recommendation Engine, Content Recommendation, Digital Receivers, User Profiling, SYSTEM
  • Dokuz Eylül University Affiliated: Yes

Abstract

In this paper, a novel method to make smart recommendations to the user is proposed using Hybrid Broadcast Broadband Television (HbbTV) standard and artificial intelligence. In order to prove this, an HbbTV application is developed to collect real television watching data and a multilayer perceptron model is trained and tested with the collected data to create user profiles. The constructed model is then used to provide recommendations to the users again using the same HbbTV application. Since HbbTV is a widely accepted public consumer electronics standard for digital receivers and most of the recent devices support it, the proposed model is device-agnostic and can reach many people using different consumer devices, which is the main motivation of this study(1).