Journal of Intelligence and Information Systems (지능정보연구)

Korea Intelligent Information System Society (한국지능정보시스템학회)
권호 표지

Personalized Recommendation System for IPTV using Ontology and K-medoids

IPTV환경에서 온톨로지와 k-medoids기법을 이용한 개인화 시스템

  • 윤병대 (인하대학교 컴퓨터 정보공학과) ;
  • 김종우 (인하대학교 컴퓨터 정보공학과) ;
  • 조용석 (건양대학교 전자정보공학과) ;
  • 강상길 (인하대학교 컴퓨터 정보공학부)
  • Received : 2010.08.14
  • Accepted : 2010.09.02
  • Published : 2010.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

As broadcasting and communication are converged recently, communication is jointed to TV. TV viewing has brought about many changes. The IPTV (Internet Protocol Television) provides information service, movie contents, broadcast, etc. through internet with live programs + VOD (Video on demand) jointed. Using communication network, it becomes an issue of new business. In addition, new technical issues have been created by imaging technology for the service, networking technology without video cuts, security technologies to protect copyright, etc. Through this IPTV network, users can watch their desired programs when they want. However, IPTV has difficulties in search approach, menu approach, or finding programs. Menu approach spends a lot of time in approaching programs desired. Search approach can't be found when title, genre, name of actors, etc. are not known. In addition, inserting letters through remote control have problems. However, the bigger problem is that many times users are not usually ware of the services they use. Thus, to resolve difficulties when selecting VOD service in IPTV, a personalized service is recommended, which enhance users' satisfaction and use your time, efficiently. This paper provides appropriate programs which are fit to individuals not to save time in order to solve IPTV's shortcomings through filtering and recommendation-related system. The proposed recommendation system collects TV program information, the user's preferred program genres and detailed genre, channel, watching program, and information on viewing time based on individual records of watching IPTV. To look for these kinds of similarities, similarities can be compared by using ontology for TV programs. The reason to use these is because the distance of program can be measured by the similarity comparison. TV program ontology we are using is one extracted from TV-Anytime metadata which represents semantic nature. Also, ontology expresses the contents and features in figures. Through world net, vocabulary similarity is determined. All the words described on the programs are expanded into upper and lower classes for word similarity decision. The average of described key words was measured. The criterion of distance calculated ties similar programs through K-medoids dividing method. K-medoids dividing method is a dividing way to divide classified groups into ones with similar characteristics. This K-medoids method sets K-unit representative objects. Here, distance from representative object sets temporary distance and colonize it. Through algorithm, when the initial n-unit objects are tried to be divided into K-units. The optimal object must be found through repeated trials after selecting representative object temporarily. Through this course, similar programs must be colonized. Selecting programs through group analysis, weight should be given to the recommendation. The way to provide weight with recommendation is as the follows. When each group recommends programs, similar programs near representative objects will be recommended to users. The formula to calculate the distance is same as measure similar distance. It will be a basic figure which determines the rankings of recommended programs. Weight is used to calculate the number of watching lists. As the more programs are, the higher weight will be loaded. This is defined as cluster weight. Through this, sub-TV programs which are representative of the groups must be selected. The final TV programs ranks must be determined. However, the group-representative TV programs include errors. Therefore, weights must be added to TV program viewing preference. They must determine the finalranks.Based on this, our customers prefer proposed to recommend contents. So, based on the proposed method this paper suggested, experiment was carried out in controlled environment. Through experiment, the superiority of the proposed method is shown, compared to existing ways.

최근 방송과 통신의 융합으로 TV에 통신이라는 기술이 접목되면서, TV 시청 형태에 많은 변화를 가져왔다. 이러한 형태의 TV 시청 변화는 서비스 선택의 폭을 넓혀주지만 프로그램을 선택을 위해 많은 시간을 투자해야 한다. 이러한 단점을 개선하기 위해서 본 논문에서는 IPTV환경에서 사용자의 다양한 콘텐츠를 제공하는 방송 환경에서 고객의 시청 정보를 바탕으로 고객 사용정보 온톨로지를 구축하고 그에 따라 고객을 k-medoids 방법을 이용해서 클러스터링 한다. 이를 바탕으로 고객이 선호하는 콘텐츠를 추천 하는 방법을 제안하였다. 실험부분에서 본 제안방법의 우수성을 기존의 방법과 비교하여 보여준다.

Keywords

References

  1. Kaufman, L. and Rousseeuw, P. J., "Finding Groups in Data : An Introduction to Cluster Analysis", Wiley, New York, 1990
  2. Balabanovic, M. and Shohm, Y., "Fab : Content- Based, Collaborative Recommendation", Communications of the ACM, Vol.40, No.3(1997), 66-72 https://doi.org/10.1145/245108.245124
  3. Konstan, J., Miller, B., Maltz, D., Herlocker, J., Gordon, L., and Riedl, J., "GroupLens : Applying Collaborative Filtering to Usenet News", Communications of the ACM, Vol.40, No.3 (1997), pp.77-87 https://doi.org/10.1145/245108.245126
  4. Goldberg, D., Nichols, D., Oki, B., Terry, D., "Using collaborative filtering to weave an information tapestry", In Communications of the ACM, Vol.35, No.12(1992), 61-70. https://doi.org/10.1145/138859.138867
  5. Yu, K., Schwaighofer, A., Tresp, V., Xu, X., and Kriegel, H. P., "Probabilistic Memory-Based Collaborative Filtering", IEEE Transaction on Knowledge and Data Engineering, Vol.16, No.1(2004), 56-69. https://doi.org/10.1109/TKDE.2004.1264822
  6. Sarwar, B., Karypis, G., Konstan, J., and Riedl, J., "Item-based Collaborative Filtering Recommendation Algorithms", In Proc. of the 10th International World Wide Web Conference, Hong Kong, 2001.
  7. Adomavicius, G., Tuzhilin, A., "Expert-Driven Validation of Rule-Based User Models in Personalization Applications", Journal of Data Mining and Knowledge Discovery, Vol. 5, No.1/2(2004), 33-58.
  8. Krulwich, B., "Lifestyle Finder : Intelligent User Profiling Using Large-Scale Demographic Data", Artificial Intelligent Magazine Vol.18, No.2(1997), 37-45.
  9. "OWL Web Ontology Language : Overview", W3C Recommendation, D. L. McGuinness and vanHarmelen, F., eds., http://www.w3.org/TR/owlfeatures owlfeatures, Feb. 2004.
  10. Benjamin, A., Leo, S., and R. Thomas, "ConTag : A Semantic Tag Recommendation System", I-SEMANTICS '07 Papers, (2007), 297-304.
  11. Aggrawall, R., Imielinski, T., Swami, A., "Mining association rules between sets of items in large databases", Proc. ACM SIGMOD Int'l Conference on Management of Data, 1994.
  12. R. O., Duda, P.E. Hart, D. G. Stork, "Pattern classification 2nd", Wileyinterscience, 2001.
  13. Y. S., Kim, S. Mitra, "Inegrated Adaptive Fuzzy clustering (IAFC) Algorithm", Proc. of the Second IEEE International Conference on Fuzzy Systems, Vol.2(1993), 1264-1268.
  14. Y. Blanco-Fernandez, J. J. Pazos-Arias, A. Gil-Solla, M. Ramos-Cabrer, and M. Lopez-Nores, "Providing entertainment by content-based filtering and semantic reasoning in intelligent recommender systems", IEEE Transactions on Consumer Electronics, Vol.54, No.2(2008), 727-735.
  15. Tsinaraki, C., Polydoros, P., Kazasis, F., Christodoulakis, S., "Ontology-based Semantic Indexing for MPEG-7 and TV-Anytime Audiovisual Content", Special issue of Multimedia Tools and Applications Journal on Video Segmentation for Semantic Annotation and Transcoding, 2004.
  16. WorNet, http:/wornet.princetion.edu/
  17. Billsus, D. and Pazzani, M. J., A Hybrid User Modelfor News Classification. In Kay J. (ed.), Proceedings of the Seventh International ConferenceonUserModeling( UM '99), Springer- Verlag, 1999, 99-108.
  18. Mukund, D., George, K., "Item-Based Top-N Recommendation Algorithms", ACM Transactions on Information System, Vol.22, No.1(2004), 143-177. https://doi.org/10.1145/963770.963776
  19. 김종우, 김주완, 윤병대, 이슬기, 강상길, "Ontology와 Clustering을 이용한 IPTV의 개인화된 프로그램 추천 시스템", 한국 소프트웨어공학 학술대회, 12권 1호(2010), 486-492.
  20. 박성준, 홍종규, 강상길, 김영국, "개인화된 방송 컨텐츠 추천을 위한 가중치 적용 Markov모델", 한국정보과학회, 12권 5호(2006), 326-338.