Automatic Metadata Annotation Based on User Preference Evaluation Patterns

Mari Saito

doi:10.20965/jaciii.2006.p0850

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JACIII Vol.10 No.6 pp. 850-858

doi: 10.20965/jaciii.2006.p0850

(2006)

Paper:

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Automatic Metadata Annotation Based on User Preference Evaluation Patterns

Mari Saito

Sony Corporation, 6-7-35 Kita-Shinagawa, Shinagawa, Tokyo 141-0001, Japan

Received:

December 18, 2005

Accepted:

March 9, 2006

Published:

November 20, 2006

Keywords:

recommendation, personalization, preference learning, content-based filtering, collaborative filtering

Abstract

In the automatic metadata annotation we propose effective in content recommendation matched to user preference, content is clustered based on user evaluation patterns for sampled content, not on the content itself. Non-sampled content is also annotated into proper clusters after metadata-based prediction. Verification of our approach using wine recommendation showed that our metadata was the most effective among many sets of metadata.

Cite this article as:

M. Saito, “Automatic Metadata Annotation Based on User Preference Evaluation Patterns,” J. Adv. Comput. Intell. Intell. Inform., Vol.10 No.6, pp. 850-858, 2006.

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References

[1] All Music Guide,
http://www.allmusic.com/ .
[2] MoodLogic,
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[4] K. Nagao, “Digital content annotation and transcoding,” Artech House Publishers, London, 2003.
[5] N. Yamamoto, M. Saito et al., “Recommendation Algorithm focused on Individual Viewpoints,” IEEE Consumer Communications and Networking Conference, 2005.
[6] J. J. Aucouturier and F. Pachet, “Music similarity measures: What’s the use,” Proceedings of the ISMIR 2002, pp. 157-163, 2002.
[7] M. Saito and K. Ohmura, “A Cognitive Model for Searching for Ill-Defined Targets on the Web: The Relationship Between Search Strategies and User Satisfaction,” Proceedings of the SIGIR-98, pp. 155-163, 1998.
[8] A. Buczak, J. Zimmerman, and K. Kurapati, “Personalization: Improving Ease-of-Use: Trust and Accuracy of TV show Recommender,” Proceedings of 2nd International Conference on Adaptive Hypermedia and Adaptive Web Based Systems, Workshop on Personalization in Future TV, 2002.
[9] U. Shardanand and P. Maes, “Social Information Filtering: Algorithms for Automating “word of Mouth”,” Proceedings of the 1995 ACM Conference on Human Factors in Computing Systems, pp. 210-217 1995.
[10] Amazon,
http://www.amazon.com/ .
[11] TiVo, Inc. TiVo: TV your way.
http://www.tivo.com/ .
[12] A. A. Freitas, “On rule interestingness measures,” Knowledge-Based Systems Journal 12(5-6), pp. 309-315, 1999.
[13] Zeta Bridge,
http://www.zeta-bridge.com/html
(in Japanese).
[14] J. De Leeuw and J. Van Rijckevorsel, “HOMALS and PRINCALSSome generalizations of principal components analysis,” Data Analysis and Informatics, E. Diday et al. (eds.), Amsterdam: North-Holland, 1980.
[15] T. Zhang, R. Ramakrishnon, and M. Livny, “BIRCH: An Efficient Data Clustering Method for Very Large Database,” Proceedings of the ACM SIGMOID Conference on Management of Data, pp. 103-114, 1996.
[16] G. V. Kass, “An Exploratory Technique for Investigation Large Quantities of Categorical Data,” Applied Statistics, 29, pp. 119-127, 1980.
[17] D. Biggs, B. Ville, and E. Suen, “A Method of Choosing Multiway Partitions for Classification and Decision Trees,” Journal of Applied Statistics, 18, 1, pp. 49-62, 1991.
[18] L. Ardissono, C. Gena, and P. Torasso, “User Modeling and Recommendation Techniques for Personalized Electronic Program Guides,” User Modeling and User-Adapted Interaction, 2004.
[19] G. Salton and C. Buckley, “Term-weighting approaches in automatic text retrieval,” Information Processing and Management, 24, pp. 513-523, 1988.
[20] G. Salton and C. Buckley, “Improving retrieval performance by relevance feedback,” Journal of American Sciety for Information Science, 41(4), pp. 288-297, 1990.
[21] W. W. Cooley and P. R. Lohnes, “Multivariate data analysis,” New York, John Wiley and Sons, Inc., 1971.
[22] T. Hofmann, “Probabilistic Latant Semantic Indexing,” Proceeding of 22nd Annual ACM Conference on Research and Development in Information Retrieval, pp. 50-57, 1999.
[23] T. Kohonen, “Self-Organization and Associative Memory,” Springer-Verlag Berlin, 1989.
[24] V. Vapnik, “The Nature og Statistical Learning Theory,” Springer, 1995.

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

[1] [1] All Music Guide,
http://www.allmusic.com/ .

[2] [2] MoodLogic,
http://www.moodlogic.com/ .

[3] [3] F. Pachet et al., “A Combinatorial approach to content-based music selection,” Proceedings of IEEE International Conference on Multimedia Computing and Systems, 1999.

[4] [4] K. Nagao, “Digital content annotation and transcoding,” Artech House Publishers, London, 2003.

[5] [5] N. Yamamoto, M. Saito et al., “Recommendation Algorithm focused on Individual Viewpoints,” IEEE Consumer Communications and Networking Conference, 2005.

[6] [6] J. J. Aucouturier and F. Pachet, “Music similarity measures: What’s the use,” Proceedings of the ISMIR 2002, pp. 157-163, 2002.

[7] [7] M. Saito and K. Ohmura, “A Cognitive Model for Searching for Ill-Defined Targets on the Web: The Relationship Between Search Strategies and User Satisfaction,” Proceedings of the SIGIR-98, pp. 155-163, 1998.

[8] [8] A. Buczak, J. Zimmerman, and K. Kurapati, “Personalization: Improving Ease-of-Use: Trust and Accuracy of TV show Recommender,” Proceedings of 2nd International Conference on Adaptive Hypermedia and Adaptive Web Based Systems, Workshop on Personalization in Future TV, 2002.

[9] [9] U. Shardanand and P. Maes, “Social Information Filtering: Algorithms for Automating “word of Mouth”,” Proceedings of the 1995 ACM Conference on Human Factors in Computing Systems, pp. 210-217 1995.

[10] [10] Amazon,
http://www.amazon.com/ .

[11] [11] TiVo, Inc. TiVo: TV your way.
http://www.tivo.com/ .

[12] [12] A. A. Freitas, “On rule interestingness measures,” Knowledge-Based Systems Journal 12(5-6), pp. 309-315, 1999.

[13] [13] Zeta Bridge,
http://www.zeta-bridge.com/html
(in Japanese).

[14] [14] J. De Leeuw and J. Van Rijckevorsel, “HOMALS and PRINCALSSome generalizations of principal components analysis,” Data Analysis and Informatics, E. Diday et al. (eds.), Amsterdam: North-Holland, 1980.

[15] [15] T. Zhang, R. Ramakrishnon, and M. Livny, “BIRCH: An Efficient Data Clustering Method for Very Large Database,” Proceedings of the ACM SIGMOID Conference on Management of Data, pp. 103-114, 1996.

[16] [16] G. V. Kass, “An Exploratory Technique for Investigation Large Quantities of Categorical Data,” Applied Statistics, 29, pp. 119-127, 1980.

[17] [17] D. Biggs, B. Ville, and E. Suen, “A Method of Choosing Multiway Partitions for Classification and Decision Trees,” Journal of Applied Statistics, 18, 1, pp. 49-62, 1991.

[18] [18] L. Ardissono, C. Gena, and P. Torasso, “User Modeling and Recommendation Techniques for Personalized Electronic Program Guides,” User Modeling and User-Adapted Interaction, 2004.

[19] [19] G. Salton and C. Buckley, “Term-weighting approaches in automatic text retrieval,” Information Processing and Management, 24, pp. 513-523, 1988.

[20] [20] G. Salton and C. Buckley, “Improving retrieval performance by relevance feedback,” Journal of American Sciety for Information Science, 41(4), pp. 288-297, 1990.

[21] [21] W. W. Cooley and P. R. Lohnes, “Multivariate data analysis,” New York, John Wiley and Sons, Inc., 1971.

[22] [22] T. Hofmann, “Probabilistic Latant Semantic Indexing,” Proceeding of 22nd Annual ACM Conference on Research and Development in Information Retrieval, pp. 50-57, 1999.

[23] [23] T. Kohonen, “Self-Organization and Associative Memory,” Springer-Verlag Berlin, 1989.

[24] [24] V. Vapnik, “The Nature og Statistical Learning Theory,” Springer, 1995.