A SMART PROGRAM RECOMMENDER SYSTEM BASED ON THE HYBRİD BROADCAST BROADBAND TELEVISION

Öz

In this paper, an improved version of smart program recommendation system based on Hybrid Broadcast Broadband Television (HbbTV) technology is proposed. The learning part which was based on the artificial neural network (ANN) has been enhanced by incorporating the genetic algorithm. Instead of assigning all users to the same ANN, clustering is introduced by utilizing preferred genre information obtained explicitly. The number of clusters is found automatically. Gathering the user data and presenting the television program recommendations to the user are realized by the HbbTV technology. The proposed system has been tested by the data from 248 people and has given successful results

Anahtar Kelimeler

• Artificial Neural Networks, Clustering Algorithms, Digital TV, Genetic Algorithms, Recommender Systems

KARMA YAYIN GENİŞBANT TELEVİZYON TABANLI AKILLI BİR PROGRAM ÖNERİCİ SİSTEMİ

Öz

Bu makalede, Karma Yayın Genişbant Televizyon (KygTV) teknolojisine dayanan akıllı program öneri sisteminin geliştirilmiş bir versiyonu sunulmuştur. Yapay sinir ağına (YSA) dayalı öğrenme kısmı, genetik algoritma eklenerek iyileştirilmiştir. Bütün kullanıcıları aynı YSA’ya atamaktansa, açık yollarla elde edilen tercih edilen tür bilgisi kullanılarak kümeleme yöntemi kullanılmıştır. Küme sayısı otomatik olarak bulunmuştur. Kullanıcı verisinin toplanması ve televizyon program önerilerinin kullanıcıya sunulması KygTV teknolojisi ile gerçeklenmiştir. Önerilen sistem 248 kişinin verisi ile test edilmiş ve sistemin başarılı sonuçlar verdiği görülmüştür

Anahtar Kelimeler

• Yapay Sinir Ağları, Kümeleme Algoritmaları, Sayısal TV, Genetik Algoritmalar, Öneri Sistemleri

Kaynakça

1. Topalli, I., Kilinc, S., 2016. Modelling User Recommendations Based on the Hybrid Broadcast Broadband Television using Neural Networks, IEEE Trans. Consum. Electron., Vol. 62, No. 2, pp. 182-190.
2. European Standard, 2015. ETSI TS 102 796 V1.3.1 - Hybrid Broadcast Broadband TV.http://www.etsi.org/deliver/etsi_ts /102800_102899/102812/01.03.01_60 /ts_102812v010301p.pdf (Retrieved on 22.12.2016).
3. Alvarez, F., Martin, C.A., Alliez, D., Roc, P.T., Steckel, P., Menendez, J.M., Cisneros, G., Jones, S.T., 2009. Audience Measurement Modeling for Convergent Broadcasting and IPTV Networks. IEEE Trans. Broadcast., Vol. 55, No. 2, pp. 502-515.
4. Adomavicius, G.,Tuzhilin, A., 2005. Toward the Next Generation of Recommender Systems: A survey of the State-of-the-Art Extensions, IEEE Trans. Knowl. Data Eng., Vol. 17, No. 6, pp. 734-749.
5. Barragáns-Martínez, A.B., Costa- Montenegro, E., Burguillo, J.C., Rey- López, M., Mikic-Fonte, F.A., Peleteiro, A., 2010. A Hybrid Content-Based and Item-Based Approach to Recommend TV Programs Enhanced Decomposition, Inf. Sci. (Ny)., Vol. 180, No. 22, pp. 4290-4311. Filtering with Singular Value
6. Bjelica, M., 2010. Towards TV Recommender System: Experiments with User Modeling, IEEE Trans. Consum. Electron., Vol. 56, No. 3, pp. 1763-1769.
7. Bjelica, M., 2011. Unobtrusive Relevance Feedback for Personalized TV Program Guides, IEEE Trans. Consum. Electron., Vol. 57, No. 2, pp. 658-663.
8. Krstic, M.,Bjelica, M., 2012. Context- Aware Personalized Program Guide Based on Neural Network, IEEE Trans. Consum. Electron., Vol. 58, No. 4, pp. 1301-1306.

9. Isobe, T., Fujiwara, M., Kaneta, H., Morita, Development of a TV Reception Navigation System Personalized with Viewing Habits, IEEE Trans. Consum. Electron., Vol. 51, No. 2, pp. 665-674.
10. Lee, S., Lee, D., Lee, S., 2010. Personalized Recommendation System under a Cloud Computing Environment, IEEE Trans. Consum. Electron., Vol. 56, No. 2, pp. 1034-1042. Program
11. Zhang, H., Zheng, S., Yuan, J., 2005. A Personalized Compliant with MHP, IEEE Trans. Consum. Electron., Vol. 51, No. 2, pp. 731-737. System
12. Jabbar, H., Jeong, T., Hwang, J., Park,G., 2008. Authentication in IPTV using RFID Technique, IEEE Trans. Consum. Electron., Vol. 54, no. 1, pp. 105–109.
13. Yang, J., Park, H., Lee, G.M., Choi, J.K., 2015.A Web-Based IPTV Content Syndication System for Personalized Content Guide, J. Commun. Networks, Vol. 17, No. 1, pp. 67-74.
14. Kim, E., Pyo, S., Park, E., Kim, M., 2011. An Automatic Recommendation Scheme of TV Program Contents for (IP)TV Personalization, IEEE Trans. Broadcast., Vol. 57, No. 3, pp. 674-684.
15. Soares, M., Viana, P., 2014. TV Recommendation and Personalization Systems: Integrating Broadcast and Video On-Demand Services, Advances in Electrical and Computer Engineering, Vol. 14, No. 1, pp. 115-120.
16. Krovi, R., 1992. Genetic Algorithms for Clustering: A Preliminary Investigation, The Twenty-Fifth Hawaii International Conference on System Sciences, 7-10 Jan, Hawaii, pp. 540-544.
17. Krishna, K., Murty, M.N., 1999. Genetic K-means Algorithm, IEEE Trans. Syst. Man, Cybern. Part B Cybern., Vol. 29, No. 3, pp. 433-439.
18. Maulik, U., Bandyopadhyay, S., 2000. Genetic Algorithm-Based Clustering Technique, Pattern Recognit., Vol. 33, pp. 1455-1465.
19. Lin, H.J., Yang, F.W., Kao, Y.T., 2005. An Efficient Technique, Tamkang J. Sci. Eng., Vol. 8, No. 2, pp. 113-122. Clustering
20. Bandyopadhyay, S., Maulik, U., 2002. Genetic Clustering for Automatic Evolution of Clusters and Application to Image Classification, Pattern Recognit., Vol. 35, No. 6, pp. 1197-1208.
21. Kudová, P., 2007. Clustering Genetic Algorithm, International Workshop on Database Applications, Regensburg, pp. 138-142. Systems DEXA, 3-7 Sept, Specification for Service
22. Sheikh, R.H., Raghuwanshi, M.M., Jaiswal, A.N., 2008. Genetic Algorithm Based Clustering: A Survey, First Int. Conf. Emerg. Trends Eng. Technol., 16- 18 July, Nagpur, Vol. 2, No. 6, pp. 314- 319.
23. Cichocki, A., Unbehauen, R., 1993. Neural Networks for Optimization and Signal Processing, 1st ed., John Wiley & Sons, Inc., New York, 544p.
24. Topalli, I., 2017. Modelling User Habits and Providing Recommendations Based on Hybrid Television Standards Using Artificial Neural Networks Together with Genetic Algorithms. Dokuz Eylül University, The Graduate School of Natural and Applied Sciences, PhD Thesis, 100p, İzmir, Turkey.