The KIPS Transactions:PartB (정보처리학회논문지B)

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Multidimensional Optimization Model of Music Recommender Systems

음악추천시스템의 다차원 최적화 모형

  • 박경수 (대구대학교 산학협력단) ;
  • 문남미 (호서대학교 벤처전문대학원 IT응용기술학과)
  • Received : 2011.11.09
  • Accepted : 2012.01.31
  • Published : 2012.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

This study aims to identify the multidimensional variables and sub-variables and study their relative weight in music recommender systems when maximizing the rating function R. To undertake the task, a optimization formula and variables for a research model were derived from the review of prior works on recommender systems, which were then used to establish the research model for an empirical test. With the research model and the actual log data of real customers obtained from an on line music provider in Korea, multiple regression analysis was conducted to induce the optimal correlation of variables in the multidimensional model. The results showed that the correlation value against the rating function R for Items was highest, followed by Social Relations, Users and Contexts. Among sub-variables, popular music from Social Relations, genre, latest music and favourite artist from Items were high in the correlation with the rating function R. Meantime, the derived multidimensional recommender systems revealed that in a comparative analysis, it outperformed two dimensions(Users, Items) and three dimensions(Users, Items and Contexts, or Users, items and Social Relations) based recommender systems in terms of adjusted $R^2$ and the correlation of all variables against the values of the rating function R.

일반적으로 추천시스템의 구성변수가 많아질수록 평가함수 R을 극대화하는 것은 유리하나 계산의 복잡성으로 예측성능과 추천유효성을 저해할 수 있어 구성변수의 증가와 추천 성능을 동시에 해결하는 것이 필요하다. 본 연구는 이러한 과제를 해결하기 위해 음악추천시스템을 대상으로 음악추천 시 평가함수 R을 극대화하기 위한 다차원 구성요소와 이들의 상대적 중요도에 대해 연구하였다. 이를 위해 관련 선행연구를 바탕으로 도출된 수식과 차원들을 이용하여 다차원 최적화 모형을 수립하고 다차원 최적관계를 도출하기 위한 실제 고객의 사용로그 자료를 활용하여 다중회귀분석을 하였다. 그 결과 음악선호평가에 있어 상품차원, 사회관계차원, 사용자차원, 상황차원 순으로 상관관계가 높은 것으로 나타났고 특히 사회관계차원의 구성변수인 인기곡과 상품차원의 구성변수인 음악장르, 최신곡 및 선호아티스트가 음악선호평가와 상관관계가 높은 것으로 나타났다. 한편 도출된 다차원 추천모형은 사용자 상품의 2차원 추천시스템 및 사용자 상품 상황 또는 사용자 상품 사회관계의 3차원 추천시스템과 성능을 비교 평가한 결과 종속변수인 평가함수 R에 대한 투입된 독립변수들인 각 차원들의 설명력이 가장 높고 또한 평가함수 R과 사용자차원, 상품차원, 상황차원 및 사회관계차원의 개별 상관관계도 더 높은 것으로 나타났다.

Keywords

References

  1. H.Y. Lee, H. Ahn, and I. Han, "VCR : Virtual Community recommender using the technology acceptance model and the user's needs type," Expert Systems with App.33, pp.984-995, 2007. https://doi.org/10.1016/j.eswa.2006.07.012
  2. K. Goldberg, T. Roeder, D. Gupta, and C. Perkins, "Eigentaste: A Constant time collaborative filtering algorithm,"Information Retrieval Journal, Vol.4, No.2, pp.133-151, 2001. https://doi.org/10.1023/A:1011419012209
  3. B. Sanwar, G. Karypies, J. Konstan, and J.Riedel, "Item based Collaborative Filtering Recommendation Algorithms," In Proc. of 10th Int'l WWW Conf. pp.285-295, 2001.
  4. G. Adomavicius, R. Sanharanarayanan, S. Sen and A. Tuzhilin, "Incorporating Contextual Information in Recommender Systems Using a Multidimensional Approach," ACM Transactions on Information Systems, Vo1.23, No.1, pp.103-145, 2005.
  5. G. Adomavicius, and A. Tuzhilin, "Context-aware recommender systems," Recommender Systems Handbook, Part 1, pp.217-253, 2011.
  6. C. Desrosiers, and G. Karypis, "A comprehensive Survey of Neighborhood based Recommendation Methods," Recommender Systems Handbook, Part 1, pp.107-144, 2011.
  7. W. Woerndl, and G. Groh, "Utilizing Physical and Social Context to Improve Recommender Systems," Web Intelligence and Intelligent Agent Technology Workshops, IEEE/WIC/ ACM Int'l Conf., pp.123-128, 2007.
  8. P. Bonacich, "Power and Centrality : A Family of Measures," American Journal of Sociology Vol.92, pp.1170-1182, 1987. https://doi.org/10.1086/228631
  9. G. Adomavicius, and A. Tuzhilin, "Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions," Knowledge and Data Engineering, IEEE Transactions on Vol.17, Iss.6, pp.734-774, 2005. https://doi.org/10.1109/TKDE.2005.99
  10. A. Schmidt, M. Beigl, and H.W. Gellersen, "There is More to Context than Location," Computers and Graphics, Vol.23, No.6, pp.893-901, 1999. https://doi.org/10.1016/S0097-8493(99)00120-X
  11. M. Benerecetti, P. Bouquet, and M. Bonifacio, "Distributed Context Aware System," Human-Computer Interaction, Vol.16, No.2, pp.213-228, 2000.
  12. A.K. Dey, and G.D. Abowd, "Towards a Better Understanding of Context and Context-Awareness," Proceedings of CHI 2000 Workshop on the What, Who, Where, When, Why, and How of Context Awareness, pp.1-6, 2000.
  13. 이동주, 이상근, 이상구, "시간 상황정보를 고려한 협업필터링을 이용한 음악추천," 한국컴퓨터종합학술대회 논문집 Vol.36, No.1, pp.123-128, 2009.
  14. 박종학, 조윤호, 김재경, "사회연결망 신규고객 추천문제의 새로운 접근," 지능정보연구, 제 15권 제 1호, pp.123-140, 2009.
  15. R. Kimbal, 'The Data Warehouse Toolkit' John Wiley & Sons, Inc., 1996.
  16. S. Chaudhuri, and U. Dayal, "An overview of data warehousing and OLAP technology," ACM SIGMOD Record, Vol.26, No.1, pp.65-74, 1997. https://doi.org/10.1145/248603.248616
  17. D. Koller, and M. Sahami, "Toward optimal feature selection," In Proc. of 13th Int'l Conf. on Machine Learning, Morgan Kaufman, pp.284-292, 1996.
  18. H. Liu, and H. Motoda, Feature Selection for Knowledge Discovery and Data Mining Kluwer Academic Publishers, 1998.
  19. S. Chatterjee, 'A.S. Hadi, and B. Price, Regression Analysis by Example', John Wiley and Sons, Inc., 2000.
  20. 이재식, 이진천, "상황 인식을 이용한 사례기반 음악추천시스템," 지능정보, 제 12권 제 3호, pp.111-126, 2006년.
  21. M.H. Park, H.S. Park, and S.B. Cho, "Restaurant recommendation for group of people in mobile environments using probabilistic multi-criteria decision making," Computer-Human Interaction, Lecture Notes in Computer Science, Vol.5068, pp.114-122, 2008.
  22. Y. Lee, K. Koazr, and K. Larsen, "The Technology Acceptance Model: Past, Present, and Future," Communications of the Assoc. for Information Systems, Vol.12, pp.752-780, 2003.
  23. 유재현, 박철, "기술수용모델 (Technology Acceptance Model) 연구에 대한 종합적 고찰," Entrue Journal of Information Technology, Vol.9, No.2, pp.31-50, 2010.
  24. 박경수, 문남미, "음악추천시스템의 수용성에 개인 감정과 상황이 미치는 영향에 관한 연구," 한국콘텐츠학회 Vol.23, No.15, pp.116-123, 2011.
  25. A. Uitdenbogerd and R. van Schyndel, "A Review of Factors Affecting Music Recommender Success," Proc. 3rd Int'l Conf. Music Information Retrieval, pp.204-208, 2002.
  26. P.Y.K. Chau, and P.J.H. Hu, "Examining a Model of Information Technology Acceptance by Individual Professionals: A Exploratory Study," Journal of Management Information Systems, Vol.18, No.4, pp.191-229, 2002. https://doi.org/10.1080/07421222.2002.11045699
  27. N. Mallata, M. Rossia, V.K. Tuunainen, and A. Oornia, "The impact of use context on mobile services acceptance: The case of mobile ticketing," Information & Management, Vol.46, Iss.3, pp.190-195, 2009. https://doi.org/10.1016/j.im.2008.11.008
  28. V. Venkatesh, M.G Morris, G.B. Davis, and F.D. Davis, "User acceptance of information technology: Toward a unified view," MIS Quarterly, Vol.27, No.3, pp.425-478, 2003.
  29. Y. Malhotra, and D.F. Galletta, "Extending the Technology Acceptance Model to Account for Social Influence: Theoretical Bases and Empirical Validation," Proceedings of the 32nd Hawaii Int'1 Conf. on System Science, 1999.
  30. D. McFarland, and D. Hamiltion, "Factors affecting student performance and satisfaction : Online versus traditional course delivery," Journal of Computer Information Systems, pp.25-32, 2006.
  31. J. Breese, D. Heckerman, and C. Kadie, "Empirical Analysis of Predictive Algorithms for Collaborative Filtering," Proc. of 4th Conf. Uncertainty in Artificial Intelligence, pp.43-52, 1998.
  32. 이학식, 임지훈, SPSS 18.0 매뉴얼, 집현재 pp.304-335, 2011
  33. A. Popescul, L.H. Ungar, D.M. Pennock, and S. Lawrence, "Probabilistic Models for Unified Collaborative and Content Based Recommendation in Sparse-Data Environments," Proc. 17th Conf. Uncertainty in Artificial Intelligence, pp.437-444, 2001.
  34. J.M. Lebreton, M.B. Hargis, B. Griepentrog, F. Oswald, and R.E. Ployhart, "A Multidimensional Approach for Evaluating Variables in Organizational Research and Practice," Personal Psychology, Vol.60, pp.475-498, 2007. https://doi.org/10.1111/j.1744-6570.2007.00080.x
  35. P. Pu, and L. Chen, "A user-Centric Evaluation Framework of Recommender Systems," Proceedings of the ACM RecSys 2010 Workshop, pp.366-369, 2010.
  36. V. Venkatesch, "Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model," Information Systems Research, Vol.11, No.4, pp.342-365, 2000. https://doi.org/10.1287/isre.11.4.342.11872
  37. F. Davis, "Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology," MIS Quarterly, pp.319-340, 1989.
  38. Z. Cataltepe and B. Altinel, Music Recommendation by Modeling User's Preferred Perspectives of Content, Singer/Genre and Popularity, Chapter X, Collaborative and Social Information Retrieval and Access : Techniques for Improved User Modeling, IGI Global, pp.203-221, 2009.
  39. C.H. Park and M.S. Kahng, "Temporal Dynamics in Music Listening Behavior: A Case Study of Online Music Service," 9th IEEE/ACIS Int'l Conf. on Computer and Information Science, pp.573-578, 2010.
  40. B. Hu, M. Guo, and H. Zhang, "A Hybrid Music Recommendation System by M-LSA," 2009 International Conference on Computational Intelligence and Natural Computing, pp.129-132, 2009.
  41. F. Wu and B. A. Huberman, "Popularity, Novelty and Attention," Proceedings of the 9th ACM Conference on Electronic Commerce, ACM, pp.240-245, 2008.
  42. D.M. Pennok, E. Horvitz, S. Lawrence and C. L. Giles, "Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach," In Proc. of 16th Conf. on Uncertainty in Artificial Intelligence, pp.478-480, 2000.
  43. H.S. Park, J.O. Yoo, and S.B. Cho, "A Context-Aware Music Recommendation System Using Fuzzy Bayesian Networks with Utility Theory," Fuzzy Systems and Knowledge Discovery, Lecture Notes in Computer Science, Vol.4223, pp.970-979, 2006.
  44. I. Konstas, V. Stathopoulos, and J.M. Jose, "On social networks and collaborative recommendation," In Proc. the 32nd ACM SIGIR Conference on Research and Development in Information Retrieval, 2009.
  45. H. Ma, I. King, and M.R. Lyu, "Learning to recommend with social trust ensemble," In Proc. the 32nd ACM SIGIR Conference on Research and Development in Information Retrieval, 2009.
  46. H.J. Ahn, "Utilizing Popularity Characteristics for Product Recommendation," International Journal of Electronic Commerce, Vol.11, No.2, pp.59-80, 2006. https://doi.org/10.2753/JEC1086-4415110203