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

Khasim Vali Dudekula (), Hussain Syed (), Mohamed Iqbal Mahaboob Basha, Sudhakar Ilango Swamykan, Purna Prakash Kasaraneni, Yellapragada Venkata Pavan Kumar, Aymen Flah and Ahmad Taher Azar
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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

Sustainability, 2023, vol. 15, issue 3, 1-18

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.

Keywords: artificial intelligence (AI); convolutional neural network (CNN); hybrid filtering; machine learning; program recommendation system; smart appliances; home automation; smart Television (TV) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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