Proposed architecture and algorithm for personalized advertising on iDTV and mobile devices

IEEE Transactions on Consumer Electronics

Volumn 54, Issue 2, 2008, Pages 709-713

  de Pessemier, Toon ^a   Deryckere, Tom ^a   Vanhecke, Kris ^a   Martens, Luc ^a  

^a GHENT UNIVERSITY   (Belgium)

Author keywords

Communities; Data mining; HDTV; Internet; Mobile communication; Mobile handsets; TV

Indexed keywords

BOOLEAN FUNCTIONS; DIGITAL ARITHMETIC; DIGITAL TELEVISION; FINANCE; INTERACTIVE COMPUTER GRAPHICS; INTERNET; METADATA; MOBILE DEVICES; PORTABLE EQUIPMENT; TELECOMMUNICATION EQUIPMENT; TELEVISION APPLICATIONS; TELEVISION BROADCASTING;

DATA SOURCING; DIGITAL MEDIA; INTERACTIVE DIGITAL TELEVISION; MOBILE APPLICATIONS; PERSONAL PROFILE; PERSONALIZATION; PROMISING TECHNIQUE; RETURN ON INVESTMENT (ROI); TARGET AUDIENCE; USER ACTIVITY;

ECONOMICS;

EID: 48749088616     PISSN: 00983063     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCE.2008.4560151     Document Type: Article

References (12)

1. H. Lee, J.-G. Kim, S.-J Yang, and J.Hong, "Personalized tv services based on tv-anytime for personal digital recorder," IEEE Trans. Consumer Electron., vol. 51, no.3, pp.885-892, Aug. 2005.
2. H. Zhang, S. Zheng, and J. Yuan, "A personalized tv guide system compliant with mhp," IEEE Trans. Consumer Electron., vol. 51, no. 2, pp. 731-737, May 2005.
3. G. Linden, B. Smith, and J. York, "Amazon.com recommendations item-to-item collaborative filtering," IEEE Internet Comput., vol. 7, no. 1, pp. 76-80, Jan/Feb 2003.
4. M. Kim, B. Lee, J. Lim, M. Kim, H. Lee, and H. Lee, "Target advertisement by reasoning TV viewer's profile," in 4th European conference on interactive television. Athens University of Economics and Business, May 2006, pp. 131-137.
5. F. Hopfgartner and J. Jose, "Evaluating the implicit feedback models for adaptive video retrieval," in Proceedings of the international workshop on Workshop on multimedia information retrieval. ACM, 2007, pp. 323-331.
6. D. Kelly and J. Teevan, "Implicit feedback for inferring user preference: A bibliography," SIGIR Forum, vol. 37, no. 2, pp. 18-28, 2003.
7. X. Zhang, J. Edwards, and J. Harding, "Personalised online sales using web usage data mining," Comput. Ind., vol. 58, no. 8-9, pp. 772-782, 2007.
8. I. H. Witten and E. Frank, Data mining: Practical machine learning tools and techniques with Java implementations. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 2000.
9. G. Karypis, "Evaluation of item-based top-n recommendation algorithms," in CIKM '01: Proceedings of the tenth international conference on Information and knowledge management. New York, NY, USA: ACM, 2001, pp. 247-254.
10. A.-T. Nguyen, N. Denos, and C. Berrut, "Improving new user recommendations with rule-based induction on cold user data," in RecSys '07: Proceedings of the 2007 ACM conference on Recommender systems New York, NY, USA: ACM, 2007, pp. 121-128.
11. Z. Yu and X. Zhou, "Tv3p: An adaptive assistant for personalized tv," IEEE Trans. Consumer Electron., vol. 50, no. 1, pp. 393-399, Feb 2004.
12. Richardson and P. Domingos, "Mining knowledge-sharing sites for viral marketing," in KDD '02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. New York, NY, USA: ACM, 2002, pp. 61-70.