Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
권호 표지
DOI QR코드

DOI QR Code

TV Program Recommender System Using Viewing Time Patterns

시청시간패턴을 활용한 TV 프로그램 추천 시스템

  • Bang, Hanbyul (Department of Electrical and Computer Engineering, Sungkyunkwan University) ;
  • Lee, HyeWoo (Department of Electrical and Computer Engineering, Sungkyunkwan University) ;
  • Lee, Jee-Hyong (Department of Electrical and Computer Engineering, Sungkyunkwan University)
  • 방한별 (성균관대학교 정보통신대학 전자전기컴퓨터공학과) ;
  • 이혜우 (성균관대학교 정보통신대학 전자전기컴퓨터공학과) ;
  • 이지형 (성균관대학교 정보통신대학 전자전기컴퓨터공학과)
  • Received : 2015.03.22
  • Accepted : 2015.09.15
  • Published : 2015.10.25
Download PDF
⟨ Previous Next ⟩

Abstract

As a number of TV programs broadcast today, researches about TV program recommender system have been studied and many researchers have been studying recommender system to produce recommendation with high accuracy. Recommender system recommends TV program to user by using metadata like genre, plot or calculating users' preferences about TV programs. In this paper, we propose a new TV program Collaborative Filtering Recommender System that exploits viewing time pattern like viewing ratio, relation with finish time and recently viewing history to calculate preference for high-quality of recommendation. To verify usefulness of our research, we also compare our method which utilizes viewing time patterns and baseline which simply recommends TV program of user's most frequently watched channel. Through this experiments, we show that our method very effectively works and recommendation performance increases.

오늘날 수많은 TV 프로그램들이 방송됨에 따라 TV 프로그램을 추천해주는 추천 시스템에 관한 연구가 시작되었으며, 추천의 정확도를 더욱 높이기 위한 연구가 현재도 활발히 진행 중이다. 추천 시스템은 장르, 줄거리 등과 같은 메타데이터를 사용하여 TV 프로그램을 추천하거나, TV 프로그램에 대한 시청자의 선호도를 계산하여 TV 프로그램을 추천한다. 본 논문에서는 추천의 정확도를 높이고자 시청비율, 종료시간과의 관계, 최근시청이력 등 시청시간의 여러 패턴을 추가로 사용하여 선호도 계산에 활용하는 협업 필터링 TV 프로그램 추천 시스템을 제안한다. 연구의 효용성을 검증하기 위해 시청시간패턴의 모든 요소를 선호도 계산에 활용한 경우와 단순히 시청자가 가장 많이 시청하는 채널을 추천하는 경우의 협업 필터링 추천 결과를 비교하였다. 실험을 통해 시청시간패턴 모든 요소를 같이 선호도 계산에 활용한 경우의 성능이 증가한 것을 확인할 수 있었다.

Keywords

References

  1. Jinoh Oh, Sungchul Kim, Jinha Kim and Hwanjo Yu, "When to recommend: A new issue on TV show recommendation," Information Sciences, pp. 261-274, 2014.
  2. Ana Belen Barragans-Martinez, Enrique Costa-Montenegro, Juan C. Burguillo, Marta Rey-Lopez, Fernando A. Mikic-Fonte and Ana Peleteiro, "A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition," Information Sciences, vol. 180, no. 22, pp. 4290-4311, 2010. https://doi.org/10.1016/j.ins.2010.07.024
  3. P. Cotter and B. Smyth, "PTV: Intelligent Personalized TV Guides," Proceedings of the Seventeenth National Conference on Artificial Intelligence, pp. 957-964, Austin, TX, USA, 2000.
  4. Kaushal Kurapati, Srinivas Gutta, David Schaffer, Jacquelyn Martino and John Zimmerman, "A Multi-Agent TV Recommender," Proceedings of the UM 2001 workshop "Personalization in Future TV, 2001.
  5. Srinivas Gutta, Kaushal Kurapati, KP Lee, Jacquelyn Martino, John Milanski, J. David Schaffer and John Zimmerman, "TV Content Recommender System," Proceedings of the 17th National Conference of AAAI, 2000.
  6. Ana Belen Barragans Martinez, Jose J. Pazos Arias, Ana Fernandez Vilas, Jorge Garcia Duque and Martin Lopez Nores, "What's on TV Tonight? An Efficient and Effective Personalized Recommender System of TV Programs," Consumer Electronics, vol. 55, no. 1, pp. 286-294, 2009. https://doi.org/10.1109/TCE.2009.4814447
  7. Hyunho Yoon, Yonggil Kang, Seongiin Lee and Soowon Lee, "User Preference based personalized Electronic Program Guide," The Korean Institute of Information Scientists and Engineers, vol. 32, no. 1, pp. 730-732, 2005.
  8. Sangwon Yoo, Hongrae Lee, Hyungdong Lee and Hyoung-Joo Kim, "A Content-based TV Program Recommender," The Korean Institute of Information Scientists and Engineers Transactions on Computing Practices, vol. 9, no. 6, pp. 683-692, 2003.
  9. Jinoh Oh, Youngchul Sung, Jinha Kim, Muhammad Humayoun, Young-Ho Park and Hwanjo Yu, "Time-dependent user profiling for TV recommendation," Cloud and Green Computing (CGC), 2012 Second International Conference on. IEEE, pp. 783-787, 2012.
  10. Roberto Turrin, Andrea Condorelli, Paolo Cremonesi and Roberto Pagano, "Time-based TV programs prediction," 1st Workshop on Recommender Systems for Television and Online Video, 2014.
  11. Badrul Sarwar, George Karypis, Joseph Konstan and John Riedl, "Item-based Collaborative Filtering Recommendation Algorithms," Proceedings of the 10th international conference on World Wide Web. ACM, pp. 285-295, 2001.
  12. Jonathan L. Herlocker, Joseph A. Konstan, Al Borchers and John Riedl, "An algorithmic framework for performing collaborative filtering," Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval. ACM, pp. 230-237, 1999.
  13. John S. Breese, David Heckerman and Carl Kadie, "Empirical Analysis of Predictive Algorithms for Collaborative Filtering," Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, pp. 43-52, 1998.
  14. Xiaoyuan Su and Taghi M. Khoshgoftaar, "A Survey of Collaborative Filtering Techniques," Advances in Artificial Intelligence, vol. 4, pp. 1-20, 2009.