User profiling for TV program recommendation based on hybrid television standards using controlled clustering with genetic algorithms and artificial neural networks
Yıl: 2020 Cilt: 28 Sayı: 3 Sayfa Aralığı: 1567 - 1583 Metin Dili: İngilizce DOI: 10.3906/elk-1909-139 İndeks Tarihi: 27-05-2020
User profiling for TV program recommendation based on hybrid television standards using controlled clustering with genetic algorithms and artificial neural networks
Öz: In this paper, an earlier method proposed by the authors to make smart recommendations utilizing artificialintelligence and the latest technologies developed for the television area is expanded further using controlled clusteringwith genetic algorithms (CCGA). For this purpose, genetic algorithms (GAs), artificial neural networks (ANNs), andhybrid broadcast broadband television (HbbTV) are combined to get the users’ television viewing habits and to createprofiles. Then television programs are recommended to the users based on that profiling. The data gathered by thedeveloped HbbTV application for previous studies are reused in this study. These data are employed to cluster users.The number of clusters is found by CCGA, a method proposed in this paper. For each cluster formed by CCGA, aseparate ANN is designed to learn the viewing habits of the users of the corresponding cluster. The weight matricesare initialized also by GA. The recommendations produced using the proposed model are then presented by the sameHbbTV application developed by the authors. Clustering with GAs gives better results when compared to the well-knownK-means clustering algorithm.
Anahtar Kelime: Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
- [1] Topalli I, Kilinc S. Modelling user habits and providing recommendations based on the hybrid broadcast broadband television using neural networks. IEEE Transactions on Consumer Electronics 2016; 62 (2): 182-190.
- [2] Topallı İ, Kılınç S. A smart program recommender system based on the hybrid broadcast broadband television. Dokuz Eylül University Faculty of Engineering Journal of Science and Engineering 2018; 20 (58): 64-74.
- [3] Topallı İ. Modelling user habits and providing recommendations based on hybrid television standards using artificial neural networks together with genetic algorithms. PhD, Dokuz Eylül University, İzmir, Turkey, 2017.
- [4] ETSI. ETSI TS 102 796 v1.3.1: Hybrid broadcast broadband TV. Paris, France: ETSI, 2015.
- [5] Haykin S. Neural Networks: A Comprehensive Foundation. Upper Saddle River, NJ, USA: Prentice Hall International Inc., 1999.
- [6] Bandyopadhyay S, Maulik U. Genetic clustering for automatic evolution of clusters and application to image classification. Pattern Recognition 2002; 35 (6): 1197-1208.
- [7] Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 2005; 17 (6): 734-749.
- [8] Barragáns-Martínez AB, Costa-Montenegro E, Burguillo JC, Rey-López M, Mikic-Fonte FA et al. A hybrid contentbased and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition. Information Sciences 2010; 180 (22): 4290-4311.
- [9] Das D, Horst H. Recommender systems for TV. In: Proceedings of 15th AAAI Conference; Madison, WI, USA; 1998. pp. 35-36.
- [10] Cotter P, Smyth B. PTV: Intelligent personalised TV guides. In: Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence; Austin, TX, USA; 2000. pp. 957-964.
- [11] Ardissono L, Gena C, Torasso P, Bellifemine F, Difino A et al. User modeling and recommendation techniques for personalized electronic program guides. In: Ardissono L, Kobsa A, Maybury MT (editors). Personalized Digital Television. Dordrecht, the Netherlands: Springer, 2004. pp. 3-26.
- [12] Wu H, Zhang Z, Yue K, Zhang B, He J et al. Dual-regularized matrix factorization with deep neural networks for recommender systems. Knowledge-Based Systems 2018; 145: 46-58.
- [13] Tang H, Lei M, Gong G, Wang J. A BP neural network recommendation algorithm based on cloud model. IEEE Access 2019; 7: 35898-35907. doi: 10.1109/ACCESS.2018.2890553
- [14] Stakhiyevich P, Huang Z. An experimental study of building user profiles for movie recommender system. In: Proceedings of the 21st IEEE International Conference on High Performance Computing and Communications; China; 2019. pp. 2559-2565. doi: 10.1109/HPCC/SmartCity/DSS.2019.00358
- [15] Yang C, Ren S, Liu Y, Cao H, Yuan Q et al. Personalized channel recommendation deep learning from a switch sequence. IEEE Access 2018; 6: 50824-50838. doi: 10.1109/ACCESS.2018.2869470
- [16] Yin X, Chen Y, Mi X, Wang H, Tang Z et al. Time context-aware IPTV program recommendation based on tensor learning. In: 2018 IEEE Global Communications Conference; Abu Dhabi, United Arab Emirates; 2018. pp. 1-15. doi: 10.1109/GLOCOM.2018.8647211
- [17] Gao Y, Wei X, Zhang G, Zhou L, Dong Z. Mining IPTV user behaviors with an enhanced LDA model. In: 2018 IEEE Global Communications Conference; Abu Dhabi, United Arab Emirates; 2018. pp. 1-20. doi: 10.1109/GLOCOM.2018.8647754
- [18] Smyth B, Cotter P. Case-studies on the evolution of the personalized electronic program guide. In: Ardissono L, Kobsa A, Maybury MT (editors). Personalized Digital Television. Dordrecht, the Netherlands: Springer, 2004. pp. 53-71.
- [19] Çataltepe Z, Uluyağmur M, Tayfur E. Feature selection for movie recommendation, Turkish Journal of Electric Engineering & Computer Science 2016; 24: 833-848. doi: 10.3906/elk-1303-189
- [20] Toz M. An improved form of the ant lion optimization algorithm for image clustering problems. Turkish Journal of Electric Engineering & Computer Science 2019; 27: 1445-1460. doi: 10.3906/elk-1703-240
- [21] Maulik U, Bandyopadhyay S. Genetic algorithm-based clustering. Pattern Recognition 2000; 33: 1455-1465.
| APA | TOPALLI İ, KILINÇ S (2020). User profiling for TV program recommendation based on hybrid television standards using controlled clustering with genetic algorithms and artificial neural networks. , 1567 - 1583. 10.3906/elk-1909-139 |
| Chicago | TOPALLI İhsan,KILINÇ Selçuk User profiling for TV program recommendation based on hybrid television standards using controlled clustering with genetic algorithms and artificial neural networks. (2020): 1567 - 1583. 10.3906/elk-1909-139 |
| MLA | TOPALLI İhsan,KILINÇ Selçuk User profiling for TV program recommendation based on hybrid television standards using controlled clustering with genetic algorithms and artificial neural networks. , 2020, ss.1567 - 1583. 10.3906/elk-1909-139 |
| AMA | TOPALLI İ,KILINÇ S User profiling for TV program recommendation based on hybrid television standards using controlled clustering with genetic algorithms and artificial neural networks. . 2020; 1567 - 1583. 10.3906/elk-1909-139 |
| Vancouver | TOPALLI İ,KILINÇ S User profiling for TV program recommendation based on hybrid television standards using controlled clustering with genetic algorithms and artificial neural networks. . 2020; 1567 - 1583. 10.3906/elk-1909-139 |
| IEEE | TOPALLI İ,KILINÇ S "User profiling for TV program recommendation based on hybrid television standards using controlled clustering with genetic algorithms and artificial neural networks." , ss.1567 - 1583, 2020. 10.3906/elk-1909-139 |
| ISNAD | TOPALLI, İhsan - KILINÇ, Selçuk. "User profiling for TV program recommendation based on hybrid television standards using controlled clustering with genetic algorithms and artificial neural networks". (2020), 1567-1583. https://doi.org/10.3906/elk-1909-139 |
| APA | TOPALLI İ, KILINÇ S (2020). User profiling for TV program recommendation based on hybrid television standards using controlled clustering with genetic algorithms and artificial neural networks. Turkish Journal of Electrical Engineering and Computer Sciences, 28(3), 1567 - 1583. 10.3906/elk-1909-139 |
| Chicago | TOPALLI İhsan,KILINÇ Selçuk User profiling for TV program recommendation based on hybrid television standards using controlled clustering with genetic algorithms and artificial neural networks. Turkish Journal of Electrical Engineering and Computer Sciences 28, no.3 (2020): 1567 - 1583. 10.3906/elk-1909-139 |
| MLA | TOPALLI İhsan,KILINÇ Selçuk User profiling for TV program recommendation based on hybrid television standards using controlled clustering with genetic algorithms and artificial neural networks. Turkish Journal of Electrical Engineering and Computer Sciences, vol.28, no.3, 2020, ss.1567 - 1583. 10.3906/elk-1909-139 |
| AMA | TOPALLI İ,KILINÇ S User profiling for TV program recommendation based on hybrid television standards using controlled clustering with genetic algorithms and artificial neural networks. Turkish Journal of Electrical Engineering and Computer Sciences. 2020; 28(3): 1567 - 1583. 10.3906/elk-1909-139 |
| Vancouver | TOPALLI İ,KILINÇ S User profiling for TV program recommendation based on hybrid television standards using controlled clustering with genetic algorithms and artificial neural networks. Turkish Journal of Electrical Engineering and Computer Sciences. 2020; 28(3): 1567 - 1583. 10.3906/elk-1909-139 |
| IEEE | TOPALLI İ,KILINÇ S "User profiling for TV program recommendation based on hybrid television standards using controlled clustering with genetic algorithms and artificial neural networks." Turkish Journal of Electrical Engineering and Computer Sciences, 28, ss.1567 - 1583, 2020. 10.3906/elk-1909-139 |
| ISNAD | TOPALLI, İhsan - KILINÇ, Selçuk. "User profiling for TV program recommendation based on hybrid television standards using controlled clustering with genetic algorithms and artificial neural networks". Turkish Journal of Electrical Engineering and Computer Sciences 28/3 (2020), 1567-1583. https://doi.org/10.3906/elk-1909-139 |
