KIISE Transactions on Computing Practices (정보과학회 컴퓨팅의 실제 논문지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
권호 표지
DOI QR코드

DOI QR Code

An Ensemble Method for Latent Interest Reasoning of Mobile Users

모바일 사용자의 잠재 관심 추론을 위한 앙상블 기법

  • Received : 2015.05.28
  • Accepted : 2015.09.24
  • Published : 2015.11.15
⟨ Previous Next ⟩

Abstract

These days, much information is provided as a list of summaries through mobile services. In this regard, users consume information in which they are interested by observing the list and not by expressing their interest explicitly or implicitly through rating content or clicking links. Therefore, to appropriately model a user's interest, it is necessary to detect latent interest content. In this study, we propose a method for reasoning latent interest of a user by analyzing mobile content consumption logs of the user. Specifically, since erroneous reasoning will drastically degrade service quality, a unanimity ensemble method is adopted to maximize precision. In this method, an item is determined as the subject of latent interest only when multiple classifiers considering various aspects of the log unanimously agree. Accurate reasoning of latent interest will contribute to enhancing the quality of personalized services such as interest-based recommendation systems.

최근 모바일 서비스에서 콘텐트를 요약 정보가 담긴 리스트 형태로 제공하는 경우가 증가하고 있다. 이에 따라 사용자가 콘텐트에 관심이 있어도 별점이나 클릭과 같은 명시적 혹은 암묵적 관심을 표현하지 않고 요약 정보를 통해 콘텐트를 소비하는 잠재 관심 표현이 대다수를 차지하게 되었다. 따라서 사용자의 관심을 파악하기 위해서는 잠재 관심 콘텐트의 추론이 필수적이다. 본 연구에서는 사용자의 모바일 상의 콘텐트 소비 로그 패턴을 분석하여 잠재 관심 콘텐트를 추론하는 기법을 제안한다. 특히, 실제 서비스에 적용 시 잘못된 관심 추론은 치명적일 수 있다는 점에서 추론의 정밀도를 극대화시키기 위해 서로 다른 특성을 반영한 다수의 분류기가 모두 동의한 경우에 잠재 관심 콘텐트로 추론하는 만장일치 앙상블 방식을 도입한다. 자체 제작한 어플리케이션으로부터 콘텐트 소비 로그를 수집하였으며 이를 이용하여 제안 방법론의 우수한 성능을 확인하였다. 이러한 잠재 관심 아이템의 정확한 도출은 사용자의 관심에 기초한 추천 시스템과 같은 개인화 서비스의 질 향상에 기여할 것이다.

Keywords

Acknowledgement

Supported by : 한국연구재단

References

  1. S. Lee, H.-K. Choi, and E. Lee, "An Analysis Method of User Preference by using Web Usage Data in User Device," Journal of KIISE: Computing Practices and Letters, Vol. 15, No. 3, pp. 189-199, 2009. (in Korean)
  2. S. Y. Yoo and O. R. Jeong, "The YouTube Video Recommendation Algorithm using Users' Social Category," Journal of KIISE, Vol. 42, No. 5, pp. 664-670, 2015. (in Korean) https://doi.org/10.5626/JOK.2015.42.5.664
  3. J. Baik, J. Sim, S. Lee, H. Moon, and S. Lee, "Temporal User Interest Pattern Modeling and New Interest Estimation," Journal of KIISE: Computing Practices and Letters, Vol. 17, No. 7, pp. 413-419, 2011. (in Korean)
  4. C. Elkan and K, Noto, "Learning Classifiers from Only Positive and Unlabeled Data," Proc. of the International Conference on Knowledge Discovery and Data Mining, pp. 213-220, 2008.
  5. Q. Guo, H. Jin, D. Lagun, S. Yuan, and E. Agichtein, "Mining Touch Interaction Data on Mobile Devices to Predict Web Search Result Relevance," Proc. of the International Conference on Research and Development in Information Retrieval, pp. 153-162, 2013.
  6. G. Jawaheer, M. Szomszor, and P. Kostkova, "Comparison of implicit and explicit feedback from an online music recommendation service," Proc. of the International Workshop on Information Heterogeneity and Fusion in Recommender Systems, pp. 47-55, 2010.
  7. J. Zhao, P. Ordonez de Pablos, and Z. Qi, "Enterprise knowledge management model based on China's practice and case study," Computers in Human Behavior, Vol. 28, No. 2, 2012.
  8. M. Claypool, P. Le, M. Wased, and D. Brown, "Implicit interest indicators," Proc. of the international conference on Intelligent user interfaces, pp. 33-40, 2001.
  9. S. Zahoor, D. Rajput, M. Bedekar, and P. Kosamkar, "Capturing, understanding and interpreting user interactions with the browser as implicit interest indicators," Proc. of the IEEE International Conference on Pervasive Computing, 2015.
  10. E. R. Nunez-Valdez, J. M. C. Lovelle, O. S. Martinez, V. Garcia-Diaz, P. O. d. Pablos, and C. E. M. Marin, "Implicit feedback techniques on recommender systems applied to electronic books," Computers in Human Behavior, Vol. 28, No. 4, pp. 1186-1193, 2012. https://doi.org/10.1016/j.chb.2012.02.001
  11. J. J. Rodriguez, L. I. Kuncheva, and C. J. Alonso, "Rotation forest: A new classifier ensemble method," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 10, pp. 1619-1630, 2006. https://doi.org/10.1109/TPAMI.2006.211
  12. C.-Y. Lee, B.-J. Lee, K.-W. On, J.-W. Ha, H.-I. Kim, and B.-T. Zhang, "Place Recognition Using Ensemble Learning of Mobile Multimodal Sensory Information," KIISE Transactions on Computing Practices, Vol. 21, No. 1, pp. 64-69, 2015. (in Korean) https://doi.org/10.5626/KTCP.2015.21.1.64
  13. B. Choi, Y. Hwang, and K. Jeong, "Machine Learning and Data Mining Techniques for Information Diffusion and Structure Analysis of Social Networks," Communications of KIISE, Vol. 32, No. 7, pp. 21-25. 2014. (in Korean)
  14. S. Ionita and E. Sofron, "The fuzzy model for aircraft landing control," Lecture Notes in Computer Science, Vol. 2275, pp. 47-54, 2002.
  15. J. R. Quinlan, "Induction of decision trees," Machine learning, Vol. 1, No. 1, pp. 81-106, 1986. https://doi.org/10.1007/BF00116251
  16. K. Wang, L. Tang, J. Han, and J. Liu, Top down fp-growth for association rule mining, Springer Berlin Heidelberg, 2002.
  17. C. Cortes and V. Vapnik, "Support-vector networks," Machine learning, Vol. 20, No. 3, pp. 273-297, 1995. https://doi.org/10.1007/BF00994018