Mining Association Rules from TV Watching Log for TV Program Recommendation

Yasufumi Takama; Shunichi Hattori

doi:10.20965/jaciii.2008.p0026

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JACIII Vol.12 No.1 pp. 26-31

doi: 10.20965/jaciii.2008.p0026

(2008)

Paper:

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Mining Association Rules from TV Watching Log for TV Program Recommendation

Yasufumi Takama and Shunichi Hattori

Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan

Received:

March 30, 2007

Accepted:

October 1, 2007

Published:

January 20, 2008

Keywords:

association rule, information recommendation, human-robot interaction, humatronics, user profile

Abstract

This paper proposes a method for extracting association rules from users’ TV watching logs, aiming at TV program recommendation. In Japan, a TV is usually located in a living room, where family members communicate each other while watching the same TV programs. Based on the choice of TV channels and conversation while watching TV, we can estimate other person’s interests and preference, which is a basis for establishing friendly relationship between others. Therefore, giving the robot a capability of recommending TV program will contribute to establish friendly communication with human partners. Furthermore, transition to digital terrestrial television broadcasting (DTTB) will bring us difficulty in finding TV program worth watching from a number of TV channels in near future. Therefore, a method for recommending TV programs will be one of the most important technologies for realizing intelligent support of our daily lives, such as a partner robot. The proposed method extracts association rules from user profiles that are generated from their TV watching logs, based on which TV programs are recommend to a target user. In order to have extensibility in terms of information resource for generating user profile, the method employs a user profile of commonly-used bookmark format. When association rules are extracted from a set of the profiles, generalization law is introduced so that variety of users’ behaviors can be reduced. Experiments are performed with actual users’ logs, and the result shows the generalization law contributes to increase the accuracy of TV program recommendation.

Cite this article as:

Y. Takama and S. Hattori, “Mining Association Rules from TV Watching Log for TV Program Recommendation,” J. Adv. Comput. Intell. Intell. Inform., Vol.12 No.1, pp. 26-31, 2008.

Data files:

References

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[15] Y. Nagai, K. Hosoda, A. Morita, and M. Asada, “A constructive model for the development of joint attention,” Journal of Japanese Society for Artificial Intelligence, Vol.15, No.4, pp. 211-229, 2003.
[16] K. Sano and H. Sayama, “BisNet: An Information Sharing System Using Bookmarks of Web Browsers,” Journal of the Japanese Society for Artificial Intelligence, Vol.20, No.4, pp. 281-288, 2005 (in Japanese).
[17] Y. Takama and S. Hattori, “Mining Association Rules for Adaptive Search Engine Based on RDF Technology,” IEEE Transaction on Industrial Electronics, Vol.54, No.2, pp. 790-796, 2007.
[18] 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.
[19] 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.
[20] 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.
[21] 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.

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

[1] [1] R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules,” Proc. of the 20th VLDB Conf., pp. 487-499, 1994.

[2] [2] N. Ando, T. Suehiro, K. Kitagaki, T. Kotoku, and W. Yoon, “RTComponent Object Model in RT-Middleware –Distributed Component Middleware for RT (Robot Technology)–,” 2005 IEEE Int. Symposium on Computational Intelligence in Robotics and Automation (CIRA2005), We-B2-5, 2005.

[3] [3] N. Ando, T. Suehiro, K. Kitagaki, T. Kotoku, and W. Yoon, “RTMiddleware: Distributed Component Middleware for RT (Robot Technology),” 2005 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS2005), pp. 3555-3560, 2005.

[4] [4] D. Brickley and R. V. Guha (Eds.), “RDF Vocabulary Description Language 1.0: RDF Schema,”
http://www.w3.org/TR/rdf-schema/ ,
2004.

[5] [5] 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.

[6] [6] 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.

[7] [7] S. Hattori, Y. Iwase, Y. Muto, and Y. Takama, “Application of Association Rules to User Preference Mining from TV Watching Log,” SCIS&ISIS2006, pp. 430-433, 2006.

[8] [8] S. Hattori and Y. Takama, “Web Information Repository Employing Adaptive Hybrid Search Engine Based on Metadata,” Proc. of the 6th Int. Conf. on Intelligent Technologies, pp. 97-101, 2005.

[9] [9] Y. Hijikata, “User Profiling Technique for Information Recommendation and Information Filtering,” Journal of the Japanese Society for Artificial Intelligence, Vol.19, No.3, pp. 365-372, 2004 (in Japanese).

[10] [10] T. Isobe, M. Fujiwara, H. Kaneta, N. Uratani, and T. Morita, “Development and Features of a TV Navigation System,” IEEE Trans. on Consumer Electronics, Vol.49, No.4, pp. 1035-1042, 2003.

[11] [11] K. Kamei, K. Funakoshi, J. Akahani, and T. Satoh, “An Inter-Personal Information Sharing Model Based on Personalized Recommendations,” Journal of the Japanese Society for Artificial Intelligence, Vol.19, No.6, pp. 540-547, 2004 (in Japanese).

[12] [12] G. Klyne and J. Carroll (Eds.), “Resource Description Framework (RDF): Concepts and Abstract Syntax,”
http://www.w3.org/TR/rdf-concepts/ ,
2004.

[13] [13] Ministry of Economy, Trade and Industry, Report of Working group on robot policy,
http://www.meti.go.jp/press/20060516002/robot-houkokusho-set.pdf ,
2006 (in Japanese).

[14] [14] Y. Muto, Y. Iwase, S. Hattori, K. Hirota, and Y. Takama, “Web Intelligence Approach for Human Robot Communication under TV Watching Environment,” SCIS&ISIS2006, pp. 426-429, 2006.

[15] [15] Y. Nagai, K. Hosoda, A. Morita, and M. Asada, “A constructive model for the development of joint attention,” Journal of Japanese Society for Artificial Intelligence, Vol.15, No.4, pp. 211-229, 2003.

[16] [16] K. Sano and H. Sayama, “BisNet: An Information Sharing System Using Bookmarks of Web Browsers,” Journal of the Japanese Society for Artificial Intelligence, Vol.20, No.4, pp. 281-288, 2005 (in Japanese).

[17] [17] Y. Takama and S. Hattori, “Mining Association Rules for Adaptive Search Engine Based on RDF Technology,” IEEE Transaction on Industrial Electronics, Vol.54, No.2, pp. 790-796, 2007.

[18] [18] 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.

[19] [19] 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.

[20] [20] 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.

[21] [21] 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.