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Convolutional Neural Network-Based Personalized Program Recommendation System for Smart Television Users

Author

Listed:
  • Khasim Vali Dudekula

    (School of Computer Science and Engineering, VIT-AP University, Amaravati 522237, Andhra Pradesh, India)

  • Hussain Syed

    (School of Computer Science and Engineering, VIT-AP University, Amaravati 522237, Andhra Pradesh, India)

  • Mohamed Iqbal Mahaboob Basha

    (School of Computer Science and Engineering, VIT-AP University, Amaravati 522237, Andhra Pradesh, India)

  • Sudhakar Ilango Swamykan

    (School of Computer Science and Engineering, VIT-AP University, Amaravati 522237, Andhra Pradesh, India)

  • Purna Prakash Kasaraneni

    (School of Computer Science and Engineering, VIT-AP University, Amaravati 522237, Andhra Pradesh, India)

  • Yellapragada Venkata Pavan Kumar

    (School of Electronics Engineering, VIT-AP University, Amaravati 522237, Andhra Pradesh, India)

  • Aymen Flah

    (Energy Processes Environment and Electrical Systems Unit, National Engineering School of Gabes, University of Gabes, Gabès 6072, Tunisia)

  • Ahmad Taher Azar

    (College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
    Faculty of Computers and Artificial Intelligence, Benha University, Benha 13518, Egypt)

Abstract

The smart home culture is rapidly increasing across the globe and driving smart home users toward utilizing smart appliances. Smart television (TV) is one such appliance that is embedded with smart technology. The users of smart TV have their interests in the programs. However, automatic recommendation of programs for user-to-user is still under-researched. Several papers discussed recommendation systems, but those are related to different applications. Even though there are some works on recommending programs to smart TV users (single-user and multi-user), they did not discuss the smart TV camera module to capture and validate the user image for recommending personalized programs. Hence, this paper proposes a convolutional neural network (CNN)-based personalized program recommendation system for smart TV users. To implement this proposed approach, the CNN algorithm is trained on the datasets ‘CelebFaces Attribute Dataset’ and ‘Labeled Faces in the Wild-People’ for feature extraction and to detect a human face. The trained CNN model is applied to the user image captured by using the smart TV camera module. Further, the captured image is matched with the user image in the ‘synthetic dataset’. Based on this matching, the hybrid filtering technique is proposed and applied; thereby the recommendation of the respective program is done. The proposed CNN algorithm has achieved approximately 95% training performance. Besides, the performance of hybrid filtering is approximately 85% from the single-user perspective and approximately 81% from the multi-user perspective. From this, it is observed that hybrid filtering outperformed conventional content-based filtering and collaborative filtering techniques.

Suggested Citation

  • Khasim Vali Dudekula & Hussain Syed & Mohamed Iqbal Mahaboob Basha & Sudhakar Ilango Swamykan & Purna Prakash Kasaraneni & Yellapragada Venkata Pavan Kumar & Aymen Flah & Ahmad Taher Azar, 2023. "Convolutional Neural Network-Based Personalized Program Recommendation System for Smart Television Users," Sustainability, MDPI, vol. 15(3), pages 1-18, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2206-:d:1046055
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    References listed on IDEAS

    as
    1. Gu, Ke & Fan, Ying & Di, Zengru, 2020. "How to predict recommendation lists that users do not like," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    2. Purna Prakash Kasaraneni & Venkata Pavan Kumar Yellapragada & Ganesh Lakshmana Kumar Moganti & Aymen Flah, 2022. "Analytical Enumeration of Redundant Data Anomalies in Energy Consumption Readings of Smart Buildings with a Case Study of Darmstadt Smart City in Germany," Sustainability, MDPI, vol. 14(17), pages 1-24, August.
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