Target Advertisement Service Using TV Viewers’ Profile Inference
Due to the limitation of broadcasting service, in general, TV programs with commercial advertisements are scheduled to be broadcasted by demographics. The uniformly provided commercial can not draw many TV viewers’ interest, which is not correspondent to the goal of the commercial. In order to solve the problem, a novel target advertisement technique is proposed in this paper. The target advertisement is a personalized advertisement according to TV viewers’ profile such as their age, gender, occupation, etc. However, viewers are usually reluctant to inform their profile to the TV program provider or the advertisement company because their information can be used on some bad purpose by unknown people. Our target advertisement technique estimates a viewer’s profile using Normalized Distance Sum and Inner product method. In the experiment, our method is evaluated for estimating the TV viewers’ profile using TV usage history provided by AC Neilson Korea.
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- 1.Marchese, M., Ronchetti, M.: New Models for Higher Educational Curricula. In: ITRE 2004, pp. 69–73 (2004)Google Scholar
- 2.Jennehag, U., Zhang, T.: Increasing Bandwidth Utilization In Next Generation Iptv Networks. In: International Conference of Image Processing, vol. 3, pp. 2075–2078 (2004)Google Scholar
- 4.Bozios, T., Lekakos, G., Skoularidou, V., Chorianopoulos, K.: Advanced Techniques for Personalised Advertising in a Digital TV Environment: The iMEDIA System. In: Proceedings of the E-business and E-work Conference, pp. 1025–1031 (2001)Google Scholar
- 5.Miyahara, K., Pazzani, M.J.: Collaborative Filtering With the Simple Bayesian Classifier. In: Sixth Pacific-Rim International Conference on Artificial Intelligence, pp. 230–237 (2000)Google Scholar
- 6.Shahabi C., Faisal A., Kashani F.B., Faruque J.: INSITE: A Tool for Interpreting Users Interaction with a Web Space. International Conference on Very Large Data Bases (2000) 635-638 Google Scholar
- 7.Terano, T., Murakami, E.: Finding Users’ Latent Interests for Recommendation by Learning Classifier Systems. In: Proceedings of Knowledge-Based Intelligent Engineering Systems and Allied Technologies, vol. 2, pp. 651–654 (2000)Google Scholar
- 8.Suh, C., Kim, W.: A Novel Measurement for Retrieval Efficiency of Image Database Retrieval System. Journal The Korean Society of Broadcast Engineers 5(1), 68–81 (2000)Google Scholar