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Contextualizing and capturing individual user interactions in shared iTV environments

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Abstract

Advances in Interactive TV (iTV) technology have enabled users to actively interact with the TV instead of just passively watching it. Associating the individual user interactions with contextual data (e.g., date, time, current channel, and people around) may reveal important information about user interests regarding the iTV content. However, capturing individual data is a difficult task since it lacks a proper mechanism to identify viewers while using the iTV. In a typical TV environment, a viewer has only a conventional remote control (RC) device being shared by other viewers, which makes it difficult to distinguish the events performed by each user. This paper presents a novel approach that facilitates the capture of contextualized and individualized data while users interact with the iTV content by using mobile devices as second screen interfaces. In contrast with conventional RCs, mobile devices are personal and typically present advanced computing and communication capabilities that makes it possible to distinguish each viewer and allows the capture of individual and contextualized interactions. The data generated in those interactions may be interpreted by specific algorithms becoming useful information for TV service providers (TSPs) enhancing TV-based services, e.g., advertising and personalization. An experimental prototype was developed as a proof of concept for the mechanism proposed in this paper. The prototype consists of an application that allows to capture and contextualize interactions of three iTV related events: channel change, sound volume change, and content evaluation.

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References

  1. ABNT (2007) ABNT (Brazilian Technical Standards Association) NBR 15603-3:2007 - Digital terrestrial television - Multiplexing and service information (SI) Part 3: Syntaxes and definitions of extension information of SI, Rio de Janeiro, Brazil

  2. ABNT (2008) ABNT (Brazilian Technical Standards Association) NBR 15607-1:2008 - Digital terrestrial television - Interactive channel Part 1: Protocols physical interfaces and software interfaces, Rio de Janeiro, Brazil

  3. Abreu J, Almeida P, Teles B, Reis M (2013) Viewer behaviors and practices in the (new) television environment. In: Proceedings of the 11th european conference on interactive TV and video - euroITV ’13, ACM Press, New York, New York, USA. doi:10.1145/2465958.2465970, pp 5–12

  4. Abreu J, Almeida P, Teles B (2014) TV discovery & enjoy: a new approach to help users finding the right TV program to watch. In: Proceedings of the 2014 ACM international conference on interactive experiences for TV and online video - TVX ’14, ACM Press, New York, New York, USA. doi:10.1145/2602299.2602313, pp 63–70

  5. Aina T, Ye Z, Dai Z, Jianghui C (2014) Field tests of two-way television audience measurement system. In: 2014 IEEE international symposium on broadband multimedia systems and broadcasting. doi:10.1109/BMSB.2014.6873536, pp 1–6

  6. Alvarez F, Martin CA, Alliez D, Roc PT, Steckel P, Menendez JM, Cisneros G, Jones ST (2009) Audience measurement modeling for convergent broadcasting and IPTV networks. IEEE Trans Broadcast 55(2):502–515. doi:10.1109/TBC.2008.2012040

    Article  Google Scholar 

  7. Bambini R, Cremonesi P, Turrin R (2012) Recommender systems for interactive TV. In: Kompatsiaris Y, Mérialdo B, Lian S (eds) TV Content analysis:techniques and applications, multimedia computing, communication and intelligence, 1st edn. CRC Press, London, pp 277–307

  8. Basilio SDCA, Moreno MF, Barrére E (2013) Supporting interaction and audience analysis in interactive TV systems. In: Proceedings of the 11th european conference on interactive TV and video - euroITV ’13, ACM Press, New York, New York, USA. doi:10.1145/2465958.2465977, pp 23–30

  9. Buschmann F, Henney K, Schmidt D (2007) Pattern-oriented software architecture: a pattern language for distributed computing, vol 4. Wiley, New York

    Google Scholar 

  10. Cabarcos PA, Guerrero RS, Mendoza FA, Diaz-Sanchez D, Marin Lopez A (2011) FamTV: an architecture for presence-aware personalized television. IEEE Trans Consum Electron 57(1):6–13. doi:10.1109/TCE.2011.5735473

    Article  Google Scholar 

  11. Carmona C, Ramírez-gallego S, Torres F, Bernal E, del Jesus M, García S (2012) Web usage mining to improve the design of an e-commerce website: OrOliveSur.com. Expert Syst Appl 39(12):11,243–11,249. doi:10.1016/j.eswa.2012.03.046

    Article  Google Scholar 

  12. Cesar P, Geerts D (2011) Past, present, and future of social TV: a categorization. In: 2011 IEEE consumer communications and networking conference (CCNC), IEEE. doi:10.1109/CCNC.2011.5766487, pp 347–351

  13. Cesar P, Chorianopoulos K, Jensen JF (2008) Social television and user interaction. Comput Entertain 6(1):1–10. doi:10.1145/1350843.1350847

    Article  Google Scholar 

  14. Courtois C, D’heer E (2012) Second screen applications and tablet users. In: Proceedings of the 10th european conference on interactive TV and video - euroiTV ’12, ACM Press, New York, New York, USA. doi:10.1145/2325616.2325646, vol 11, pp 153–156

  15. Dai L, Wang Z, Yang Z (2012) Next-generation digital television terrestrial broadcasting systems: Key technologies and research trends. IEEE Commun Mag 50 (6):150–158

    Article  Google Scholar 

  16. De Lucena V, Filho J, Viana N, Maia O (2009) A home automation proposal built on the Ginga digital TV middleware and the OSGI framework. IEEE Trans Consum Electron 55(3):1254–1262. doi:10.1109/TCE.2009.5277985

    Article  Google Scholar 

  17. Hellman E (2013) Android programming: pushing the limits, 1st edn. Wiley, West Sussex

    Google Scholar 

  18. Hwang MC, Ha L, Kim NH, Park CS, Ko SJ (2007) Person identification system for future digital TV with intelligence. IEEE Trans Consum Electron 53 (1):218–226. doi:10.1109/TCE.2007.339528

    Article  Google Scholar 

  19. Kazienko P, Adamski M (2007) AdROSA–adaptive personalization of web advertising. Inf Sci 177(11):2269–2295. doi:10.1016/j.ins.2007.01.002

    Article  Google Scholar 

  20. Kim J, Kang S (2011) An ontology-based personalized target advertisement system on interactive TV. Multimed Tools Appl 64(3):517–534. doi:10.1007/s11042-011-0965-0

    Article  Google Scholar 

  21. Kulesza R, Meira SR, Ferreira TP, Alexandre ES, Filho GL, Neto MCM, aS Santos C (2012) A model-driven approach for integration of interactive applications and web services: a case study in interactive digital TV platform. In: 2012 IEEE international conference on multimedia and expo workshops, IEEE. doi:10.1109/ICMEW.2012.52. VIC, Melbourne, pp 266–271

  22. Lai CF, Chang JH, Hu CC, Huang YM, Chao HC (2011) CPRS: a cloud-based program recommendation system for digital TV platforms. Futur Gener Comput Syst 27(6):823–835. doi:10.1016/j.future.2010.10.002

    Article  Google Scholar 

  23. Lee BH, Kim DH (2012) Efficient context-aware selection based on user feedback. IEEE Trans Consum Electron 58(3):978–984. doi:10.1109/TCE.2012.6311345

    Article  Google Scholar 

  24. Lee S, Lee D, Lee S (2010) Personalized DTV program recommendation system under a cloud computing environment. IEEE Trans Consum Electron 56(2):1034–1042. doi:10.1109/TCE.2010.5506036

    Article  Google Scholar 

  25. Lim C, Choi JH, Nam SW, Chang JH (2014) A new television audience measurement framework using smart devices. Multimed Tools Appl 73:1757–1776. doi:10.1007/s11042-013-1658-7

    Article  Google Scholar 

  26. de Lucena V, Viana N, Maia O, Filho J, da Silva W (2012) Designing an extension API for bridging Ginga iDTV applications and home services. IEEE Trans Consum Electron 58(3):1077–1085. doi:10.1109/TCE.2012.6311359

    Article  Google Scholar 

  27. Lukic N, Teslic N, Maruna T, Mihic V (2013) A Java API interface for the search of DTV services in embedded multimedia devices. IEEE Trans Consum Electron 59(4):875–882. doi:10.1109/TCE.2013.6689702

    Article  Google Scholar 

  28. Masthoff J (2004) Group modeling: selecting a sequence of television items to suit a group of viewers. User Model User-Adapt Interact 14(1):37–85. doi:10.1023/B:USER.0000010138.79319.fd

    Article  Google Scholar 

  29. Morris S, Smith-Chaigneau A (2005) Interactive TV standards: a guide to MHP, OCAP and javaTV. Focal Press, Boston

    Google Scholar 

  30. Nielsen (2013) Action figures: How second screens are transforming TV viewing. http://www.nielsen.com/us/en/insights/news/2013/action-figures--how-second-screens-are-transforming-tv-viewing.html, Accessed 14 March 2016

  31. Pijetlovic S, Jovanov N, Vukobrat V, Basicevic I (2014) One solution of a RESTful API for a cloud based DTV content provider. In: 2014 IEEE Fourth International Conference on Consumer Electronics Berlin (ICCE-Berlin), IEEE, Berlin, Germany. doi:10.1109/ICCE-Berlin.2014.7034253, pp 384–387

  32. Rao KR, Bojkovic ZS, Milovanovic DA (2006) Introduction to multimedia communications. Wiley, Hoboken

    Google Scholar 

  33. Robertson S, Wharton C, Ashworth C, Franzke M (1996) Dual device user interface design: PDAs and interactive television. In: Proceedings of the SIGCHI conference on human factors in computing systems common ground - CHI ’96, ACM Press, New York, New York, USA, pp 79–86, DOI 10.1145/238386.238408, (to appear in print)

  34. Romero C, Espejo PG, Zafra A, Romero JR, Ventura S (2013) Web usage mining for predicting final marks of students that use Moodle courses. Comput Appl Eng Educ 21(1):135–146. doi:10.1002/cae.20456

    Article  Google Scholar 

  35. Sadalage PJ, Fowler M (2012) NoSQL distilled: a brief guide to the emerging world of polyglot persistence, 1st edn. Addison-Wesley Professional, Boston

    Google Scholar 

  36. Sanchez F, Barrilero M, Alvarez F, Cisneros G (2013) User interest modeling for social TV-recommender systems based on audiovisual consumption. Multimed Syst 19:493–507. doi:10.1007/s00530-013-0312-6

    Article  Google Scholar 

  37. Senkul P, Salin S (2011) Improving pattern quality in web usage mining by using semantic information. Knowl Inf Syst 30(3):527–541. doi:10.1007/s10115-011-0386-4

    Article  Google Scholar 

  38. Soares LFG, Costa RM, Moreno MF, Moreno MF (2009) Multiple exhibition devices in DTV systems

  39. Srivastava J, Cooley R, Deshpande M, Pn Tan (2000) Web usage mining: discovery and applications of usage patterns from web data. ACM SIGKDD Explorations Newsletter 1(2):12–23. doi:10.1145/846183.846188

    Article  Google Scholar 

  40. Srivastava H (2001) Interactive TV technology and markets. Artech House, Norwood, MA, USA

  41. Teixeira CAC, Melo EL, Cattelan RG, Pimentel M DGC (2010) Taking advantage of contextualized interactions while users watch TV. Multimed Tools Appl 50(3):587–607. doi:10.1007/s11042-010-0481-7

    Article  Google Scholar 

  42. Tsekleves E, Whitham R, Kondo K, Hill A (2011) Investigating media use and the television user experience in the home. Entertain Comput 2(3):151–161. doi:10.1016/j.entcom.2011.02.002

    Article  Google Scholar 

  43. Wang B, Wang J, Lu H (2013) Exploiting content relevance and social relevance for personalized ad recommendation on internet TV. ACM Trans Multimed Comput Commun Appl 9(4):26:1–26:23. doi:10.1145/2501643.2501648

    Article  Google Scholar 

  44. Wheeler W, White J (2013) Spring in practice, 1st edn. Manning Publications, New York

    Google Scholar 

  45. Witten IH, Frank E (2011) Data mining: practical machine learning tools and techniques, 3rd edn. Morgan Kaufmann Publishers, Burlington

    MATH  Google Scholar 

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Acknowledgments

This work was developed at Electronics and Information Technology R&D Center (CETELI) at the Federal University of Amazonas (UFAM). The authors would like to thank the following Brazilian agencies: Fundação de Amparo à Pesquisa do Estado do Amazonas (FAPEAM), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for their financial support.

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Authors and Affiliations

  1. Graduate Program in Electrical Engineering, Federal University of Minas Gerais, Av. Antônio Carlos 6627, 31270-901, Belo Horizonte, MG, Brazil

    Ricardo Erikson V. de S. Rosa

  2. PPGEE, PPGI and CETELI at UFAM, and PPGEE at UFMG, Manaus, Amazonas, Brazil

    Vicente Ferreira de Lucena Jr.

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Correspondence to Ricardo Erikson V. de S. Rosa.

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Erikson V. de S. Rosa, R., Ferreira de Lucena, V. Contextualizing and capturing individual user interactions in shared iTV environments. Multimed Tools Appl 76, 8573–8595 (2017). https://doi.org/10.1007/s11042-016-3489-9

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