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장르유사도와 선호장르를 이용한 협업필터링 설계

Collaborative Filtering Design Using Genre Similarity and Preffered Genre

  • 김경록 (호서대학교벤처전문대학원 IT응용기술학과) ;
  • 변재희 (호서대학교벤처전문대학원 IT응용기술학과) ;
  • 문남미 (호서대학교벤처전문대학원 IT응용기술학과)
  • Kim, Kyung-Rog (Dept. of IT Application Tech., Hoseo Graduate School of Venture) ;
  • Byeon, Jae-Hee (Dept. of IT Application Tech., Hoseo Graduate School of Venture) ;
  • Moon, Nam-Mee (Dept. of IT Application Tech., Hoseo Graduate School of Venture)
  • 투고 : 2010.06.23
  • 심사 : 2011.02.18
  • 발행 : 2011.04.30

초록

전자상거래와 소셜미디어 서비스의 활성화에 따라, 집단지성을 개인 맞춤 서비스에 활용하는 추천시스템에 관한 연구가 활발히 진행되고 있다. 또한, 스마트폰의 발달과 모바일 환경의 발달에 따라 단말의 제약성에도 불구하고 개인화 서비스에 대한 연구가 가속화되고 있다. 대표적인 예로 위치기반 서비스와의 결합이다. 이에 본 연구에서는 영화의 장르유사도와 선호장르를 이용한 추천시스템을 제안한다. 영화 장르 유사도 프로파일을 생성하여 이를 모바일실험 환경에서 서비스 될 수 있도록 설계하고 프로토 타이핑 한 후에 MovieLens 데이터를 적용하여 평가한다.

As e-commerce and social media service evolves, studies on recommender systems advance, especially concerning the application of collective intelligence to personalized custom service. With the development of smartphones and mobile environment, studies on customized service are accelerated despite physical limitations of mobile devices. A typical example is combined with location-based services. In this study, we propose a recommender system using movie genre similarity and preferred genres. A profile of movie genre similarity is generated and designed to provide related service in mobile experimental environment before prototyping and testing with data from MovieLens.

키워드

참고문헌

  1. M.D.Mulvenna and S.S.Anand and A.G.Buchner, "Personalization on the Net Using Web Mining : Introduction," Communications of the ACM, Vol. 43, No. 8, pp. 122-125, 2000. https://doi.org/10.1145/345124.345165
  2. H.J. Kwon and D.K.Chung, K.S.Hong, "A Multimedia Recommender System Using User Playback Time," Korean Society for Internet Information, Vol. 10, No. 1, pp.111-121, 2009.
  3. J.W.Choi, "An Application for Calculation and Visualization of Narrative Relevance of Films Using Keyword Tags," Korea Advanced Institute of Science and Technlogy : Master's Thesis, pp.19-21, 2007.
  4. K.Chorianopoulos, "Personalized and mobile digital TV applications," Multimedia tools and Applications, Vol. 36, pp.1-10, 2008. https://doi.org/10.1007/s11042-006-0081-8
  5. T.Q.Lee, Y.Park, Y.T.Park, "A time-based approach to effective recommender systems using implicit feedback," Expert Systems with Applications, Vol. 34, Issue. 4, pp.3055-3062, May 2008. https://doi.org/10.1016/j.eswa.2007.06.031
  6. K.R.Kim, J.H.Lee, J.H.Byeon, N.M. Moon, "Recommender System Using the Movie Genre Similarity in Mobile Service," The 4th International Conference on Multimedia and Ubiquitous Engineering, 2010.
  7. J.M.Oh, J.H.Song, N.M.Moon, "Preference Element Selectable Interactive Recommender System by Employing Collaborative Filtering," The 4th International Conference on Multimedia and Ubiquitous Engineering, 2010.
  8. J.Y.Park and B.S. Chon, "Analysis on Mobile Content Services of the Demestic Media Comoanies," The Journal of the Korea Contents Association, Vol. 10, No. 1, pp.160-169, 2009. https://doi.org/10.5392/JKCA.2010.10.1.160
  9. H.S.Park and M.H.Park, S.B.Cho, "LBS Information Recommendation in Mobile Environment Using Multi-Criteria Decision Making," Journal of Computing Science and Engineerin, Vol. 14, No. 3, pp. 306-310, 2008.
  10. B.J.An and E.J.Kim, Y.B.Lee, "A Hiererchical Representatives Clustering Technique for Data Mining," Korean Institute of Information Scientists and Engineers, Vol. 27, No. 2, pp.69-71, 2000.
  11. M.H.Huh and Y.G.Lee, "Reproducibility estimation and Application of K-means clustering," The Korean Journal of Applied Statistics, Vol. 17, No. 1, pp.135-144, 2004. https://doi.org/10.5351/KJAS.2004.17.1.135
  12. D.J.Lee and S.K.Lee, S.G.Lee, "Considering temporal context in music recommendation based on collaborative filtering," Proceedings of Korea computer congress, Vol. 36, No. 1, 2009.
  13. G.Adomavicius and A.Tuzhilin, "Toward the Next Generation of Recommender Systems : A Survey of the State-of-the-Art and Possible Extensions," IEEE Transactions on Knowledge and Data Engineering, Vol. 17, No. 6, pp. 734-749, June 2005. https://doi.org/10.1109/TKDE.2005.99
  14. H.C. Lee, "Enhancement of Collaborative Filtering in Electronic Commerce Recommender System," Kangwon University Graduation School : Doctoral thesis, 2009.
  15. S.H.Jo, "Weight Recommendation Technique Based on Item Quality To Improve Performance of New User Recommendation and Recommendation on The Web, " Hannam University Graduation School : Doctoral thesis, 2008.
  16. G.Lekakos and G.M.Giaglis, "Improving the Prediction Accuracy of Recommendation Algorithms : Approaches Anchored on Human Factors," Interacting with Computers, Vol. 18, pp. 410-431. 2006. https://doi.org/10.1016/j.intcom.2005.11.004
  17. S.J.Lee and T.R.Jeon, G.D,Baek, S.S.Kim, "A Movie Rating Prediction System of User Propensity Analysis based on Collaborative Filtering and Fuzzy System," Journal of Korean institute of intelligent systems, Vol. 19, No. 2, pp.242-247, 2009. https://doi.org/10.5391/JKIIS.2009.19.2.242
  18. K.C.Park, "Collaborative Filtering Method Considering Purchase Interval for Mobile Multimedia Contents Recommendation," Hanyang University : Master's Thesis, 2008.
  19. J.S.Lee and S.D.Park, "Performance Improvement of a Movie Recommendation System using Genre-wise Collaborative Filtering," Journal of intelligent information systems, Vol.13, No. 4, pp.65-78, 2007.
  20. B.M.Sarwar and G.Karypis, J.Konstan, J.Riedl, "Item-Based Collaborative Filtering Recommendation Algorithms," ,Proceedings of the 10th international conference on World Wide Web, pp.285-295, 2001.
  21. H.S.Lee, J.H.Kwon, "Collaborative Filtering Mobile Contents Recommender Application Using Context and Folksonomy," Journal of Korean Institute of Information Technology, Vol. 7, No.2, pp.132-140, April 2009.
  22. Y.H.Cho and J.K.Kim, "Application of Web usage mining and product taxonomy to collaborative recommendations in e-commerce," Expert System with Applications, Vol 26(1), pp.233-246, 2004. https://doi.org/10.1016/S0957-4174(03)00138-6
  23. J.K.Kim and Y.H.Cho, S.T.Kim, H.K.Kim, "A Personalized Recommender System for Mobile Commerce Application," The Korea Society of Management Information Systems, Vol. 15, No. 3, pp.223-240, 2005.
  24. P.Melville, R.J.Mooney, R.Nagarajan, "Content- Boosted Collaborative Filtering for Improved Recommendations," AAAI-02, pp.187-192, 2002.
  25. Y.Zhang, W.Song, "A Collaborative Filtering Recommendation Algorithm Based on Item Genre and Rating Similarity," 2009 International Conference on Computational Intelligence and Natural Computing, pp. 72-75, 2009.
  26. B.I.Kwon, N.M.Moon, "Recommendation system for supporting self-directed learning on e-learning marketplace," Journal of The Korea Society of Computer and Information, Vol. 15, No. 2, pp.135-146, 2010. https://doi.org/10.9708/jksci.2010.15.2.135
  27. B.M. Sarwar and G.Karypis, J.Konstan, J.Riedl, "Application of Dimensionality Reduction in Recommender System : A Case Study," WebKDD-2000 Workshop, 2000.
  28. B.M. Sarwar and G.Karypis, J.Konstan, J.Riedl, "Recommender Systems for Large-scale E-Commerce : Scalable Neighborhood Formation Using Clustering," 5th International Conference on Computer and Information Technology, 2002.

피인용 문헌

  1. A Study on Personalized Music Recommendation Model through Analysis on Users' Music Preference Factors vol.19, pp.11, 2011, https://doi.org/10.9728/dcs.2018.19.11.2041
  2. A Simple and Effective Combination of User-Based and Item-Based Recommendation Methods vol.15, pp.1, 2019, https://doi.org/10.3745/jips.01.0036