An efficient ad recommendation system for TV programs (original) (raw)

References

  1. Billsus, D., Pazzani, M.: A hybrid user model for news story classification. In: Proc. of Seventh Int. Conf. on User Modeling, June 1999
  2. Shardanand, U., Maes, P.: Social information filtering: algorithms for automating word of mouth. In: Proc. of Human Factors in Computing Systems (CHIć695) Conf, pp. 210–217. ACM Press, New York (1995)
  3. Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User Adapt. Interact. 12(4), 331–370 (2002)
    Article MATH Google Scholar
  4. Bhargava, H.K., Sridhar, S., Herrick, C.: Beyond spreadsheets: tools for building decision support systems. IEEE Comput. 32(3), 31–39 (1999)
    Google Scholar
  5. Bae, S.-M., Park, S.-C.: Fuzzy web ad selector based on web usage mining. In: IEEE Conf. on Intelligent Systems (2003)
  6. Lekakos, G., et al.: A lifestyle-based approach for delivering personalised advertisements in digital iTV. J. Comput. Mediat. Comm., January (2004)
  7. Kurapati, K., Gutta, S., Schaffer, D., Martino, J., Zimmerman, J.: A multi-agent TV recommender. Workshop on Personalization in Future TV, Germany (2001)
  8. Masthoff, J.: Group modeling: selecting a sequence of television items to suit a group of viewers. User Model. User Adapt. Interact. (UMUAI) Journal 14, 37–85 (2004)
    Article Google Scholar
  9. Kosala, R., Blockeel, H.: Web mining research—a survey. SIGKDD Explor. 2, 1–15 (2000)
    Article Google Scholar
  10. Amit, T., Srividya, G., Sridhar, V.: ITV application for intent tracking. In: Proc. of 2nd European Conf. on Interactive TV (2004)
  11. Zimmerman, J., Kurapati, K., Buczak, A.L., Schaffer, D., Gutta, S., Martino, J.: TV persolalization system—design of a TV show recommender engine and interface, Chap. 5. Persolaized Digital Television Book
  12. He, Z.-Y., Xu, X.-F., Deng, S.-C., Dong, B.: K-histograms: an efficient clustering algorithm for categorical dataset. In: ARXIV, September 2005
  13. Blanco-Fernandez, Y., Pazos-Arias, J.J., Gil-Solla, A., Ramos-Cabrer, M., Lopez-Nores, M., Barragans-Martinez, B.: AVATAR: a multi-agent TV recommender system using MHP applications. In: Proc. of e-Technology, e-Commerce and e-Service (2005)
  14. Dukes, A.: The adverstising market in a product oligopoly. J. Indus. Econ. 52(3), 327–348 (2004)
    Article Google Scholar
  15. https://www.google.com/adsense/support/bin/answer.py?answer=32757&topic=8526 http://msdn2.microsoft.com/en-us/library/
  16. Rovira, M., et al.: IndexTV: a MPEG-7 based personlised recommentation system for digital TV. In: Proc. of IEEE Intl. Conf. on Multimedia and Expo (2004)
  17. Miller, G., Backwith, R., Fellbuam, C., Gross, D., Miller, K.: Introduction to WordNet—an online lexical database. Int. J. Lexicogr.
  18. Chen, H., Dumais, S.T.: Bringing order to the web: automatically categorizing search results. In: CHI’00 Proc. of the SIGCHI Conf. on Human factors in computing systems. ACM press, New York (2000)
  19. Shen, D., Pan, R., Sun, J.T., Pan, J.J., Wu, K., Yin, J., Yang, Q.: Our winning solution to query classification in KDDCUP2005. SIGKDD Newsletter (2005)
  20. He, Z., Xu, X., Deng, S.: Squeezer: an efficient clustering algorithm for categorical data. J. Comput. Sci. Technol. 17, 611–624 (2002)
    Article MATH MathSciNet Google Scholar
  21. Guha, S., et al.: ROCK: a robust clustering algorithm for categorical attributes. In: Proc. of Int. Conf. on Data Engineering, pp. 512–521, March 1999
  22. Huang, Z.: Extensions to the K-means algorithm for clustering large datasets with categorical values. Data Min. Knowl. Discov.2, September 1998
  23. Huang, Z.: A fuzzy K-modes algorithm for clustering categorical data. IEEE Trans. Fuzzy Syst. 7(4), (1999)
  24. Frank, H., Frank, K., Rudolf, K., Thomas, R.: Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition. Wiley Publications, London (1999)
    MATH Google Scholar
  25. Bollapragada, S., Bussieck, M., Mallik, S.: Scheduling commercial videotapes in broadcast television. Oper. Res. November 2004
  26. Subodha, K., Jacob, V.S., Chelliah, S.: Scheduling advertisements on a Web page to maximize revenue. Eur. J. Oper. Res. 1067–1089, September 2005
  27. Saha, A., Pal, M., Pal, T.K.: Selection of programme slots of television channels for giving advertisement: A graph theoretic approach. Inf. Sci. 2480–2492, January 2007
  28. Kimms, A., Müller Bungart, M.: Revenue management for broadcasting commercials: the channelć6s problem of selecting and scheduling the advertisements to be aired. Int. J. Rev. Manage. 1(1), 28–44 (2007)
    Article Google Scholar
  29. Benoist, T., Bourreau, E., Rottembourg, B.: The TV-break packing problem. In: Eur. J. Oper. Res. 1371–1386, December (2005)
  30. Ali, K., van Stam, W.: TiVo: Making Show Recommendations Using a Distributed Collaborative Filtering Architecture. KDD’04, August 2004
  31. O’Connor, M., Herlocker, J.: Clustering items for collaborative filtering. In: SIGIR Workshop on Recommender Systems (2001)
  32. Bollapragada, S., Mallik, S.: A mathematical programming based approach to scheduling commercials in broadcast television. Tecnical Report, GE Research and Developement Center, November 2001

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