Profile Generation for TV Program Recommendation Based on Utterance Analysis

Yasufumi Takama; Yuki Muto

doi:10.20965/jaciii.2009.p0086

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JACIII Vol.13 No.2 pp. 86-90

doi: 10.20965/jaciii.2009.p0086

(2009)

Paper:

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Profile Generation for TV Program Recommendation Based on Utterance Analysis

Yasufumi Takama and Yuki Muto

Tokyo Metropolitan University, Tokyo, Japan

Received:

July 1, 2008

Accepted:

January 15, 2009

Published:

March 20, 2009

Keywords:

TV program recommendation, information recommendation, sentiment analysis

Abstract

This paper proposes a method for generating user profile that is to be used for TV program recommendation. The proposed method does not estimate user’s interest in a TV program only based on its watching time as most of existing methods do, but also based on user’s utterances by applying sentiment analysis. Three kinds of scores are calculated for each watched program, based on which fuzzy inference is performed to estimate its rating. After the estimation, profile structure is obtained by generating category and subcategory layers. This paper mainly focuses on estimation of users’ ratings on TV programs. Experiments are performed with test subjects, and the results show the proposed method can improve the estimation accuracy of ratings compared with existing approach using only watching time. Although the proposed method includes language-dependent processing, it is expected the core of estimation rating can be applied to other languages.

Cite this article as:

Y. Takama and Y. Muto, “Profile Generation for TV Program Recommendation Based on Utterance Analysis,” J. Adv. Comput. Intell. Intell. Inform., Vol.13, No.2, pp. 86-90, 2009.

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References

[1] Y. Takama, “Introduction of Humatronics – Towards Integration of Web Intelligence and Robotics,” CISIM2007, pp. 37-44, 2007.
[2] A. Taylor and R. Harper, “Switching On to Switch Off: a Analysis of Routine TV Watching Habits and Their Implications for Electronic Programme Guide Design,” usableiTV, 1, pp. 7-13, 2002.
[3] Y. B. Fernandez, J. J. P. Arias, M. L. Nores, A. G. Solla, and M. R. Cabrer, “AVATAR: An Improved Solution for Personalized TV based on Semantic Inference,” IEEE Transactions on Consumer Electronics, Vol.52, No.1, pp. 223-231, 2006.
[4] T. Isobe, M. Fujiwara, H. Kaneta, N. Uratani, and T. Morita, “Development and Features of a TV Navigation Sys-tem,” IEEE Trans. on Consumer Electronics, Vol.49, No.4, pp. 1035-1042, 2003.
[5] H. Zhang, S. Zheng, and J. Yuan, “A Personalized TV Guide System Compliant with MHP,” IEEE Transactions on Consumer Electronics, Vol.51, No.2, pp. 731-737, 2005.
[6] H. Zhang and S. Zheng, “Personalized TV Program Recommendation based on TV-Anytime Metadata,” Proc. of the Ninth Int. Symposium on Consumer Electronics (ISCE 2005), pp. 242-246, 2005.
[7] Y. Zhiwen and X. Zhou, “TV3P: An Adaptive Assistant for Personalized TV,” IEEE Transactions on Consumer Electronics, Vol.50, No.1, pp. 393-399, 2004.
[8] N. Daita, J. M. Pires, M. Cardoso, and H. Pita, “Temporal Patterns of TV Watching for Portuguese Viewers,” 2005 Portuguese Conf. on Artificial Intelligence, pp. 151-158, 2005.
[9] T. Inui and M. Okumura, “A Survey of Sentiment Analysis,” Journal of Natural Language Processing, Vol.13, No.3, pp. 201-241, 2006 (in Japanese).

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] Y. Takama, “Introduction of Humatronics – Towards Integration of Web Intelligence and Robotics,” CISIM2007, pp. 37-44, 2007.

[2] [2] A. Taylor and R. Harper, “Switching On to Switch Off: a Analysis of Routine TV Watching Habits and Their Implications for Electronic Programme Guide Design,” usableiTV, 1, pp. 7-13, 2002.

[3] [3] Y. B. Fernandez, J. J. P. Arias, M. L. Nores, A. G. Solla, and M. R. Cabrer, “AVATAR: An Improved Solution for Personalized TV based on Semantic Inference,” IEEE Transactions on Consumer Electronics, Vol.52, No.1, pp. 223-231, 2006.

[4] [4] T. Isobe, M. Fujiwara, H. Kaneta, N. Uratani, and T. Morita, “Development and Features of a TV Navigation Sys-tem,” IEEE Trans. on Consumer Electronics, Vol.49, No.4, pp. 1035-1042, 2003.

[5] [5] H. Zhang, S. Zheng, and J. Yuan, “A Personalized TV Guide System Compliant with MHP,” IEEE Transactions on Consumer Electronics, Vol.51, No.2, pp. 731-737, 2005.

[6] [6] H. Zhang and S. Zheng, “Personalized TV Program Recommendation based on TV-Anytime Metadata,” Proc. of the Ninth Int. Symposium on Consumer Electronics (ISCE 2005), pp. 242-246, 2005.

[7] [7] Y. Zhiwen and X. Zhou, “TV3P: An Adaptive Assistant for Personalized TV,” IEEE Transactions on Consumer Electronics, Vol.50, No.1, pp. 393-399, 2004.

[8] [8] N. Daita, J. M. Pires, M. Cardoso, and H. Pita, “Temporal Patterns of TV Watching for Portuguese Viewers,” 2005 Portuguese Conf. on Artificial Intelligence, pp. 151-158, 2005.

[9] [9] T. Inui and M. Okumura, “A Survey of Sentiment Analysis,” Journal of Natural Language Processing, Vol.13, No.3, pp. 201-241, 2006 (in Japanese).