Personalised Advertising Supported by Agents

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 217)


This paper reports the development of a B2B platform for the personalization of the publicity transmitted during the program intervals. The platform as a whole must ensure that the intervals are filled with ads compatible with the profile, context and expressed interests of the viewers. The platform acts as an electronic marketplace for advertising agencies (content producer companies) and multimedia content providers (content distribution companies). The companies, once registered at the platform, are represented by agents who negotiate automatically the price of the interval timeslots according to the specified price range and adaptation behaviour. The candidate ads for a given viewer interval are selected through a matching mechanism between ad, viewer and the current context (program being watched) profiles. The overall architecture of the platform consists of a multiagent system organized into three layers consisting of: (i) interface agents that interact with companies; (ii) enterprise agents that model the companies, and (iii) delegate agents that negotiate a specific ad or interval. The negotiation follows a variant of the Iterated Contract Net Interaction Protocol (ICNIP) and is based on the price/s offered by the advertising agencies to occupy the viewer’s interval.


Multiagent system B2B Multimedia Brokerage Profile Matching Fixed ICNIP Web Services 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    López-Nores, M., et al.: MiSPOT: dynamic product placement for digital TV through MPEG-4 processing and semantic reasoning. Knowledge and Information Systems 22(1), 101–128 (2010)CrossRefGoogle Scholar
  2. 2.
    NoTube, NoTube (2012), (accessed in February 2013)
  3. 3.
    LinkedTV, LinkedTV (2012), (accessed in February 2013)
  4. 4.
    HBB-NEXT, HBB-NEXT Next Generation Media (2012), (accessed in February 2013)
  5. 5.
    de Sousa, L.V., Malheiro, B., Foss, J.: Negotiation platform for personalised advertising. In: de Strycker, L. (ed.) Proceedings of the Fifth International European Conference on the Use of Modern Information and Communication Technologies (ECUMICT 2012), pp. 361–373 (2012)Google Scholar
  6. 6.
    Foss, J.D., Malheiro, B., Burguillo, J.C.: Personalised placement in networked video. In: Proceedings of the 21st International Conference Companion on World Wide Web, WWW 2012 Companion, pp. 959–968. ACM, New York (2012)CrossRefGoogle Scholar
  7. 7.
    British Broadcasting Corporation. Program categories, (accessed in September 2012)
  8. 8.
    Yellow Pages. Classified ads categories, (accessed in September 2012)
  9. 9.
    Madylova, A., Oguducu, S.: A taxonomy based semantic similarity ofdocuments using the cosine measure. In: Proceedings of the 24th International Symposium on Computer and Information Sciences (ISCIS 2009), pp. 129–134 (2009)Google Scholar
  10. 10.
    Foundation for Intelligent Physical Agents, FIPA iterated contract net interaction protocol specification, FIPA TC Communication, Standard 30 (2002), (accessed in September 2012)

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Bruno Veloso
    • 1
    • 2
  • Luís Sousa
    • 1
  • Benedita Malheiro
    • 1
    • 2
  1. 1.Instituto Superior de Engenharia do PortoInstituto Politécnico do PortoPortoPortugal
  2. 2.INESC TEC – INESC Technology and Science (formerly INESC Porto)PortoPortugal

Personalised recommendations