DOI QR코드

DOI QR Code

콘텐츠들 간의 유의어 태그매핑을 이용한 확장된 추천기법의 연구

A Study of Extended Recommendation Method Using Synonym Tags Mapping Between Two Types of Contents

  • 투고 : 2016.09.22
  • 심사 : 2016.12.28
  • 발행 : 2017.01.01

초록

Recently recommendation methods need personalization and diversity as well as accuracy whereas the traditional researches have been mainly focused on the accuracy of recommendation in terms of quality. The diversity of recommendation is also important to people in terms of quantity in addition to quality since people's desire for content consumption have been stronger rapidly than past. In this paper, we pay attention to similarity of data gathered simultaneously among different types of contents. With this motivation, we propose an enhanced recommendation method using correlation analysis with considering data similarity between two types of contents which are movie and music. Specifically, we regard folksonomy tags for music as correlated data of genres for movie even though they are different attributes depend on their contents. That is, we make result of new recommendation movie items through mapping music folksonomy tags to movie genres in addition to the recommendation items from the typical collaborative filtering. We evaluate effectiveness of our method by experiments with real data set. As the result of experimentation, we found that the diversity of recommendation could be extended by considering data similarity between music contents and movie contents.

키워드

참고문헌

  1. F. C. Surprenant and M. R. Solomon, "Predictability and Personalization in the Service Encounter", Journal of Marketing, Vol. 51, No. 2, pp. 86-96, April 1987. https://doi.org/10.2307/1251131
  2. P. Riddle, "Tags: What are They Good for?", The University of Texas at Austin, 2005.
  3. D. Kim, B. Lee and C. Kim, "A Study on Tag Cloud Architecture as a Dynamic Navigation Link" Journal of Advanced Information Technology and Convergence, Vol. 9 No. 8, pp. 203-211, August 2011.
  4. D. Weinberger, "Tagging and Why It Matters", Harvard University, Berkman Center Reserarch Publication July 2005.
  5. B. Sarwar, G. Karypis, J. Konstan, and J. Riedle, "Analysis of Recommendation Algorithms for E-Commerce," Proceedings of the 2nd ACM conference on Electronic commerce, pp. 158-167, 2000.
  6. X. Su and T. M. Khoshgoftaar, "Collaborative Filtering for Multi-Class Data Using Bayesian Networks," International Journal on Artificial Intelligence Tools, Vol. 17, No. 1, pp. 71-85, 2008. https://doi.org/10.1142/S0218213008003789
  7. M. A. Ghazanfar and A. Prugel-Bennett, "Leveraging Clustering Approaches to Solve the Gray-Sheep Users Problem in Recommender System," Expert Systems with Applications, Vol. 41 No. 7, pp. 3261-3275, 2014. https://doi.org/10.1016/j.eswa.2013.11.010
  8. W. H. Jeong, S. J. Kim, D. S. Park and J. Kwak, "Improved Movie Recommendation System based on Personal Propensity and Secure Collaborative Filtering", Journal of Korean Information Processing Systems, Vol. 9, No. 1, pp. 157-162, September 2013. https://doi.org/10.3745/JIPS.2013.9.1.157
  9. J. Park, Y. Jo and J. Kim "Social Network : A Novel Approach to New Customer Recommendations", korea Intelligent Information System Society, Journal of Intelligence and Information Systems, Vol. 15, No. 1, pp. 123-140, Mar 2009.
  10. M. Kim, K. Kim "Recommender Systems using Structural Hole and Collaborative Filtering", Journal of Intelligent Information System, Vol. 20, No. 4, pp. 107-120, Dec. 2014. https://doi.org/10.13088/jiis.2014.20.4.107
  11. G. Ozbal, H. Karaman, F. and Nur, "Alpaslan: A Content-Boosted Collaborative Filtering Approach for Movie Recommendation Based on Local and Global Similarity and Missing Data Prediction", Computational Journal, Vol. 54, No. 9, pp. 1535-1546, 2011. https://doi.org/10.1093/comjnl/bxr001
  12. Y. Oh, "An Expert Recommendation Technique using Hybrid Collaborative Filtering in SNS", Thesis for master's degree at the Graduate School in Chonbuk National University, 2012.
  13. O. Lee and Y. Baek, "Hybrid Preference Prediction Technique Using Weighting based Data Reliability for Collaboraive Filtering Recommendation System", Journal of The Korea Society of Computer and Information, Vol. 19. No. 5, pp. 61-69, May 2014. https://doi.org/10.9708/jksci.2014.19.5.061
  14. D. Kim, K. Lee and H. Kim, "Improved Tag Selection for Tag-cloud using the Dynamic Characteristics of Tag Co-occurrence", Journal of Korean Information Science Society, Vol. 15, No. 6, pp. 405-413, June 2009.
  15. H. Lee and M. M. Sohn, "Ontology-based Dynamic Data-Catalog Construction using Tag Cloud", Proceedings of KIISE, pp. 149-155, May 2012.
  16. GroupLens, a research lab at the University of Minnesota, https://movielens.org/join/pick-groups
  17. Last.FM, http://www.last.fm/api
  18. Thesaurus, http://www.thesaurus.com