Skip to main content
Springer Nature Link
Log in

Receiver-side semantic reasoning for digital TV personalization in the absence of return channels

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Experience has proved that interactive applications delivered through Digital TV must provide personalized information to the viewers in order to be perceived as a valuable service. Due to the limited computational power of DTV receivers (either domestic set-top boxes or mobile devices), most of the existing systems have opted to place the personalization engines in dedicated servers, assuming that a return channel is always available for bidirectional communication. However, in a domain where most of the information is transmitted through broadcast, there are still many cases of intermittent, sporadic or null access to a return channel. In such situations, it is impossible for the servers to learn who is watching TV at the moment, and so the personalization features become unavailable. To solve this problem without sacrificing much personalization quality, this paper introduces solutions to run a downsized semantic reasoning process in the DTV receivers, supported by a pre-selection of material driven by audience stereotypes in the head-end. Evaluation results are presented to prove the feasibility of this approach, and also to assess the quality it achieves in comparison with previous ones.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. http://www.dvb.org/technology/standards.

  2. http://eng.t-dmb.org.

  3. http://www.dvb-h.org.

  4. http://www.qualcomm.com/mediaflo.

  5. As explained for a server-based personalization engine in [10], the DOI for a given item can be explicitly entered by the viewer, or inferred from indirect measures such as the time he/she spends watching or executing it.

  6. The DOI of the attribute Age 14-16 is not influenced by \(\mathcal{P}_2\), because this program is not rated in \(\mathcal{S}_1\).

  7. For example, a carousel containing 4 MBytes of data, transmitted at a rate of 256 Kbps, takes 128 s to complete a cycle; so, it may take more than 2 min to load a particular piece of information.

  8. In a real scenario, the disappearance of a stereotype would mean that the group of viewers that it aims to represent should be a residual audience for the TV program in question.

  9. ATLAS is an acronym for “Architecture for T-Learning interActive Services”.

  10. http://www.mhp.org.

  11. ATTOS is an acronym for “ATlas, TOol Support”.

  12. http://www.dvb.org/technology/standards/.

  13. http://protege.stanford.edu/.

  14. http://backstage.bbc.co.uk.

  15. http://www.imdb.com.

  16. Thanks to the reuse capabilities of ATLAS (see [41]), 1.5 MBytes sufficed to deliver up to 20 courses at the same time.

  17. Interestingly, we could check that the ratings given by the viewers to courses they had classified as potentially appealing were lower than 5 only in 8% of the cases. This fact undoubtedly supports the validity of our estimation of recall.

  18. http://www.itu.int/ITU-T/IPTV/.

References

  1. Antoniou G, van Harmelen F (2004) A semantic web primer. MIT, Cambridge

    Google Scholar 

  2. Anyanwu K, Sheth A (2003) ρ-Queries: enabling querying for semantic associations on the semantic web. In: Proceedings of 12th international world wide web conference, Budapest, 20–24 May 2003

  3. Ardissono L, Gena C, Torasso P, Bellifemine F, Chiarotto A, Difino A, Negro B (2004) User modeling and recommendation techniques for personalized electronic program guides. In: Personalized digital television. Targeting programs to individual users. Kluwer, Dordrecht

    Google Scholar 

  4. Ardissono L, Kobsa A, Maybury M (eds) (2004) Personalized digital television. Targeting programs to individual users. Kluwer, Norwell

    Google Scholar 

  5. Balabanović M, Shoham Y (1997) Fab: content-based collaborative recommender. Commun ACM 40:66–72

    Article  Google Scholar 

  6. Basu C, Hirsh H, Cohen W (1998) Recommendation as classification: using social and content-based information in recommendation. In: Proceedings of the 15th national conference on artificial intelligence, Madison, 26–30 July 1998

  7. Blanco-Fernández Y, Pazos-Arias JJ, Gil-Solla A, Ramos-Cabrer M, López-Nores M, García-Duque J, Fernández-Vilas A, Díaz-Redondo RP, Bermejo-Muñoz J (2007) AVATAR: enhancing the personalized television by semantic inference. Int J Pattern Recogn Artif Intell 21:397–422

    Article  Google Scholar 

  8. Blanco-Fernández Y, Pazos-Arias JJ, Gil-Solla A, Ramos-Cabrer M, López-Nores M, García-Duque J, Díaz-Redondo RP, Fernández-Vilas A, Bermejo-Muñoz J (2008) A flexible semantic inference methodology to reason about user preferences in knowledge-based recommender systems. Knowl Based Syst 21:305–320

    Article  Google Scholar 

  9. Blanco-Fernández Y, Pazos-Arias JJ, Gil-Solla A, Ramos-Cabrer M, López-Nores M, García-Duque J, Fernández-Vias A, Díaz-Redondo RP, Bermejo-Muñoz J (2008) An MHP framework to provide intelligent personalized recommendations about Digital TV contents. Softw Pract Exp. doi:10.1002/spe.855

  10. Blanco-Fernández Y, Pazos-Arias JJ, López-Nores M, Gil-Solla A, Ramos-Cabrer M (2006) AVATAR: an improved solution for personalized TV based on semantic inference. IEEE Trans Consum Electron 52:223–231

    Article  Google Scholar 

  11. Bobillo F, Delgado M, Gómez-Romero J (2008) Representation of context-dependant knowledge in ontologies: a model and an application. Expert Syst Appl. doi:10.1016/j.eswa.2007.08.090

  12. Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User-Adapt Interact 12:331–370

    Article  MATH  Google Scholar 

  13. Cao Y, Li Y (2007) An intelligent fuzzy-based recommendation system for consumer electronic products. Expert Syst Appl 33:230–240

    Article  Google Scholar 

  14. Cheung B, Hui L, Zhang J, Yiu SM (2003) SmartTutor: an intelligent tutoring system in web-based adult education. J Syst Softw 68:11–25

    Article  Google Scholar 

  15. Chorianopoulos K (2008) Personalized and mobile digital TV applications. Multimed Tools Appl 36:1–10

    Article  Google Scholar 

  16. Difino A, Negro B, Chiarotto A (2002) A multi-agent system for a personalized electronic program guide. In: Proceedings of the 2nd workshop on personalization in future TV, Malaga, 29–31 May 2002

  17. Digital Tech Consulting Inc (2006) Digital TV receivers: worldwide history and forecasts (2000–2010). http://www.dtcreports.com/report_dtvr.aspx

  18. Foltz PW, Dumais ST (1992) Personalized information delivery: an analysis of information filtering methods. Commun ACM 35:51–60

    Article  Google Scholar 

  19. Ganesan P, Garcia-Molina H, Widom J (2003) Exploiting hierarchical domain structure to compute similarity. ACM Trans Inf Syst 21:64–93

    Article  Google Scholar 

  20. Ghaneh M (2004) System model for t-learning application based on home servers (PDR). Broadcast Technol. 19. http://www.nhk.or.jp/strl/publica/bt/en/rep0019.pdf

  21. Haase P, Ehrig M, Hotho A, Schnizler B (2004) Personalized information access in a bibliographic peer-to-peer system. In: Proceedings of the AAAI workshop on semantic web personalization, San Jose, 25–26 July 2004

  22. Hung L (2005) A personalized recommendation system based on product taxonomy for one-to-one marketing online. Expert Syst Appl 29:383–392

    Article  Google Scholar 

  23. IEEE Learning Technology Standards Committee (2002) IEEE standard for learning object metadata. IEEE standard 1484.12.1

  24. Im KH, Park SC (2007) Case-based reasoning and neural network based expert system for personalization. Expert Syst Appl 32:77–85

    Article  Google Scholar 

  25. IMS Global Learning (2005) IMS learner information packaging information model v1.0.1. http://www.imsglobal.org

  26. Karvounarakis G, Alexaki S, Christophides V, Plexousakis D, Scholl M (2002) RQL: a declarative query language for RDF. In: Proceedings of the 11th world wide web conference, Honolulu, 7–11 May 2002

  27. Lacal J (ed) (2007) Proceedings of the 1st workshop of the IEEEP2407 personalized health informatics working group. http://www.IEEE2407.org

  28. Lee J, Kim M, Lee Y (1993) Information retrieval based on conceptual distance in IS-A hierarchies. J Doc 49:188–207

    Article  Google Scholar 

  29. Lee WP, Yang TH (2003) Personalizing information appliances: a multi-agent framework for TV programme recommendations. Expert Syst Appl 25:331–341

    Article  Google Scholar 

  30. Lekakos G, Giaglis G (2004) A lifestyle-based approach for delivering personalised advertisements in digital interactive television. J Comput Mediat Commun 9

  31. Leung CW, Chan SC, Chung FL (2006) A collaborative filtering framework based on fuzzy association rules and multiple-level similarity. Knowl Inf Syst 10:357–381

    Article  Google Scholar 

  32. Lim J, Kim M, Lee B, Kim M, Lee H, Lee HK (2008) A target advertisement system based on TV viewer’s profile reasoning. Multimed Tools Appl 36:11–35

    Article  Google Scholar 

  33. López-Nores M, Pazos-Arias JJ, García-Duque J, Blanco-Fernández Y (2008) A smart medicine manager delivering health care to the networked home and beyond. In: Proceedings of the international conference on health informatics, Funchal, 28–31 January 2008

  34. Lougos C, Vassilopoulou C, Vrechopoulos A (2002) Interactive digital TV services - viewers’ perceptions. In: Proceedings of the eBusiness and eWork conference, Prague, 16–18 October 2002

  35. Maybury M, Greiff W, Boykin S, Ponte J, McHenry C, Ferro L (2004) Personalcasting: tailored broadcast news. In: Personalized digital television. Targeting programs to individual users. Kluwer, Norwell

    Google Scholar 

  36. McGuinness D, van Harmelen F (2004) OWL Web Ontology Language overview. W3C Recommendation

  37. Miller L, Seaborne A, Reggiori A (2002) Three implementations of SquishQL, a simple RDF query language. In: Proceedings of the 1st international semantic web conference, Sardinia, 10–12 June 2002

  38. Mobasher B, Jin X, Zhou Y (2004) Semantically-enhanced collaborative filtering on the web. In: Web mining: applications and techniques. Idea Group. Hershey (PA), USA

    Google Scholar 

  39. Morris S, Smith-Chaigneau A (2005) Interactive TV standards. Focal Press, St. Louis

    Google Scholar 

  40. Pazos-Arias JJ, Delgado Kloos C, López-Nores M (eds) (2008) Personalization of interactive multimedia services: a research and development perspective. Nova Science, Hauppauge

    Google Scholar 

  41. Pazos-Arias JJ, López-Nores M, García-Duque J, Díaz-Redondo RP, Blanco-Fernández Y, Ramos-Cabrer M, Gil-Solla A, Fernández-Vilas A (2008) Provision of distance learning services over interactive digital TV with MHP. Comput Educ 50:927–949

    Article  Google Scholar 

  42. Pazzani M (1999) A framework for collaborative, content-based and demographic filtering. Artif Intell Rev 13:393–408

    Article  Google Scholar 

  43. Potonnieé O (2004) A decentralized privacy-enabling TV personalization framework. In: Proceedings of the 2nd European conference on interactive television, Brighton, 31 March–2 April 2004

  44. Rada R, Mili H, Bicknell E, Blettnet M (1989) Development and application of a metric on semantic nets. IEEE Trans Syst Man Cybern 19:17–30

    Article  Google Scholar 

  45. Resnik P (1999) Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language. J Artif Intell Res 11:95–130

    MATH  Google Scholar 

  46. Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J (1994) Grouplens: an open architecture for collaborative filtering of netnews. In: Proceedings of the ACM conference on computer-supported cooperative work, Chapel Hill, 22-26 October 1994

  47. Sintek M, Decker S (2002) TRIPLE: a query, inference and transformation language for the Semantic Web. In: Proceedings of the 1st international semantic web conference, Sardinia, 10–12 June 2002

  48. Smyth B, Cotter P (1999) Surfing the digital wave: generating personalized TV listings using collaborative, case-based recommendation. In: Proceedings of the 3rd international conference on case-based reasoning, Munich, 27–30 July 1999

  49. Staab S, Studer R (eds) (2003) Handbook on ontologies. Springer, New York

    Google Scholar 

  50. Trifonova A, Ronchetti M (2005) Hoarding content in m-learning context. In: Proceedings of the 3rd IEEE conference on pervasive computing and communications workshops, Kauai Island, 8–12 March 2005

  51. Tsunoda T, Hoshino M (2008) Automatic metadata expansion and indirect collaborative filtering for TV program recommendation system. Multimed Tools Appl 36:37–54

    Article  Google Scholar 

  52. TV-Anytime forum (2003) TV-anytime specification series. ETSI standard TS 102 822

  53. Tzouveli P, Mylonas P, Kollias S (2008) An intelligent e-learning system based on learner profiling and learning resources adaptation. Comput Educ 51:224–238

    Article  Google Scholar 

  54. Velusamy S, Gopal L, Varatharajan S, Bhatnagar S (2007) Fuzzy clustering based ad recommendation for TV programs. In: Proceedings of the 5th European conference on interactive TV, Amsterdam, 24–25 May 2007

  55. Woerndl W, Schlichter J (2007) Introducing context into recommender systems. In: Proceedings of the AAAI workshop on recommender systems in e-commerce, Vancouver, 22–23 July 2007

  56. Zhang H, Zheng S, Yuan J (2005) A personalized TV guide system compliant with MHP. IEEE Trans Consum Electron 51:731–737

    Article  Google Scholar 

  57. Zimmerman J, Kurapati K, Buczak AL, Schaffer D, Gutta S, Martino J (2004) TV personalization system. Design of a TV show recommender engine and interface. In: Personalized digital television. Targeting programs to individual users. Kluwer, Norwell

    Google Scholar 

Download references

Acknowledgements

This work has been partially funded by the Ministerio de Educación y Ciencia (Gobierno de España) project TSI2007-61599, by the Consellería de Educación e Ordenación Universitaria (Xunta de Galicia) incentives file 2007/000016-0, and by the Consellería de Innovación, Industria e Comercio (Xunta de Galicia) project PGIDIT05PXIC32204PN.

Author information

Authors and Affiliations

  1. Department of Telematics Engineering, University of Vigo, ETSE Telecomunicación, Campus Universitario s/n, Vigo, 36310, Spain

    Martín López-Nores, Yolanda Blanco-Fernández, José J. Pazos-Arias, Jorge García-Duque, Manuel Ramos-Cabrer, Alberto Gil-Solla, Rebeca P. Díaz-Redondo & Ana Fernández-Vilas

Authors
  1. Martín López-Nores
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Yolanda Blanco-Fernández
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. José J. Pazos-Arias
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Jorge García-Duque
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Manuel Ramos-Cabrer
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Alberto Gil-Solla
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Rebeca P. Díaz-Redondo
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Ana Fernández-Vilas
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Martín López-Nores.

Rights and permissions

Reprints and permissions

About this article

Cite this article

López-Nores, M., Blanco-Fernández, Y., Pazos-Arias, J.J. et al. Receiver-side semantic reasoning for digital TV personalization in the absence of return channels. Multimed Tools Appl 41, 407–436 (2009). https://doi.org/10.1007/s11042-008-0239-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-008-0239-7

Keywords