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The Scientific World Journal
Volume 2014, Article ID 679849, 11 pages
http://dx.doi.org/10.1155/2014/679849
Research Article

N-Screen Aware Multicriteria Hybrid Recommender System Using Weight Based Subspace Clustering

Department of Information & Communication, Korea Aerospace University, Goyang 412-791, Republic of Korea

Received 21 February 2014; Revised 23 May 2014; Accepted 16 June 2014; Published 24 July 2014

Academic Editor: Martin Lopez-Nores

Copyright © 2014 Farman Ullah et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. C. Yoon, T. Um, and H. Lee, “Classification of N-Screen Services and its standardization,” in Proceedings of the 14th International Conference on Advanced Communication Technology (ICACT '12), pp. 597–602, IEEE, February 2012. View at Scopus
  2. J. W. Kim, F. Ullah, S. C. Lee, S. K. Jo, H. W. Lee, and W. Ryu, “Dynamic addition and deletion of device in N-screen environment,” in Proceedings of the 4th International Conference on Ubiquitous and Future Networks (ICUFN '12), pp. 118–122, Phuket, Thailand, July 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. Interacti ve Advertisement Bureau Report, http://www.iab.net/media/file/The_Multiscreen_Marketer.pdf.
  4. A. Catellier, M. Pinson, W. Ingram, and A. Webster, “Impact of mobile devices and usage location on perceived multimedia quality,” in Proceedings of the 4th International Workshop on Quality of Multimedia Experience (QoMEX '12), pp. 39–44, July 2012. View at Publisher · View at Google Scholar · View at Scopus
  5. R. Gong and H. Xu, “Impacts of appearance parameters on perceived image quality for mobile-phone displays,” Optik, vol. 125, no. 11, pp. 2554–2559, 2014. View at Publisher · View at Google Scholar
  6. J. Ben Schafer, J. A. Konstan, and J. Riedl, “Ecommerce recommendation applications,” Data Mining and Knowledge Discovery, vol. 5, pp. 115–153, 2001. View at Google Scholar
  7. D. Goldberg, D. Nichols, B. M. Oki, and D. Terry, “Using collaborative filtering to weave an information tapestry,” Communications of the ACM, vol. 35, no. 12, pp. 61–70, 1992. View at Publisher · View at Google Scholar
  8. P. Resnick and H. R. Varian, “Recommender systems,” Communications of the ACM, vol. 40, no. 3, pp. 56–58, 1997. View at Google Scholar · View at Scopus
  9. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Item-based collaborative filtering recommendation algorithms,” in Proceedings of the 10th International Conference on World Wide Web (WWW '01), pp. 285–295, 2001. View at Publisher · View at Google Scholar
  10. L. Lü, M. Medo, C. H. Yeung, Y. Zhang, Z. Zhang, and T. Zhou, “Recommender systems,” Physics Reports, vol. 519, no. 1, pp. 1–49, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. P. Pu, L. Chen, and R. Hu, “Evaluating recommender systems from the user's perspective: survey of the state of the art,” User Modelling and User-Adapted Interaction, vol. 22, no. 4-5, pp. 317–355, 2012. View at Publisher · View at Google Scholar · View at Scopus
  12. G. Shani and A. Gunawardana, “Evaluating recommendation systems,” in Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Eds., pp. 257–297, Springer, New York, NY, USA, 2011. View at Google Scholar
  13. M. Balabanović and Y. Shoham, “Fab: content-based, collaborative recommendation,” Communications of the ACM, vol. 40, no. 3, pp. 66–72, 1997. View at Google Scholar · View at Scopus
  14. M.-S. Shang, L. Y. Lu, Y.-C. Zhang, and T. Zhou, “Empirical analysis of web-based user-object bipartite networks,” Europhysics Letters, vol. 90, no. 4, p. 48006, 2009. View at Google Scholar
  15. A. A. Kardan and M. Ebrahimi, “A novel approach to hybrid recommendation systems based on association rules mining for content recommendation in asynchronous discussion groups,” Information Sciences, vol. 219, pp. 93–110, 2013. View at Publisher · View at Google Scholar · View at Scopus
  16. A. Mikeli, D. Apostolou, and D. Despotis, “A multi-criteria recommendation method for interval scaled ratings,” in Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), November 2013.
  17. R. Burke, “Hybrid recommender systems: survey and experiments,” User Modelling and User-Adapted Interaction, vol. 12, no. 4, pp. 331–370, 2002. View at Publisher · View at Google Scholar · View at Scopus
  18. J. Delgado, “Memory-Based Weighted-Majority Prediction for Recommender Systems,” 1999.
  19. F. Ullah, G. Sarwar, S. C. Lee, Y. K. Park, K. D. Moon, and J. T. Kim, “Hybrid recommender system with temporal information,” in Proceedings of the 26th International Conference on Information Networking (ICOIN '12), pp. 421–425, February 2012. View at Publisher · View at Google Scholar · View at Scopus
  20. C. Yin and Q. Peng, “A careful assessment of recommendation algorithms related to dimension reduction techniques,” Knowledge-Based Systems, vol. 27, pp. 407–423, 2012. View at Publisher · View at Google Scholar · View at Scopus
  21. J. S. Breese, D. Heckerman, and C. Kadie, “Empirical analysis of predictive algorithm for collaborative filtering,” in Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 43–52, 1998.
  22. J. Figueira, S. Greco, and M. Ehrogott, Multiple Criteria Decision Analysis: State of the Art Surveys, Springer, New York, NY, USA, 2005.
  23. K. Lakiotaki, N. F. Matsatsinis, and A. Tsoukiàs, “Multicriteria user modeling in recommender systems,” IEEE Intelligent Systems, vol. 26, no. 2, pp. 64–76, 2011. View at Publisher · View at Google Scholar · View at Scopus
  24. M. Nilashi, O. B. Ibrahim, and N. Ithnin, “Hybrid recommendation approaches for multi-criteria collaborative filtering,” Expert Systems with Applications, vol. 41, no. 8, pp. 3879–3900, 2014. View at Google Scholar
  25. X. Chen, X. Xu, J. Huang, and Y. Ye, “TW-k-means: automated two-level variable weighting clustering algorithm for multiview data,” IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 4, pp. 932–944, 2013. View at Google Scholar
  26. G. Guo, S. Chen, and L. Chen, “Soft subspace clustering with an improved feature weight self-adjustment mechanism,” International Journal of Machine Learning and Cybernetics, vol. 3, no. 1, pp. 39–49, 2012. View at Publisher · View at Google Scholar · View at Scopus
  27. Q. Li and B. M. Kim, “An approach for combining content-based and collaborative filters,” in Proceedings of the 6th International Workshop on Information Retrieval with Asian Languages, pp. 17–24, 2003.
  28. M. E. J. Newman, Mon te Carlo Methods in Statistical Physics, Oxford University Press, New York, NY, USA, 1999. View at MathSciNet
  29. G.-R. Xue, C. Lin, Q. Yang et al., “Scalable collaborative filtering using cluster-based smoothing,” in Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '05), pp. 114–21, ACM, Salvador, Brazil, 2005. View at Publisher · View at Google Scholar
  30. C. Tsai and C. Chiu, “Developing a feature weight self-adjustment mechanism for a K-means clustering algorithm,” Computational Statistics & Data Analysis, vol. 52, no. 10, pp. 4658–4672, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  31. Yahoo! Movies Dataset, http://webscope.sandbox.yahoo.com.
  32. Devices Information, http://www.androidphonesarena.com/.
  33. G. Adomavicius and Y. Kwon, “New recommendation techniques for multicriteria rating systems,” IEEE Intelligent Systems, vol. 22, no. 3, pp. 48–55, 2007. View at Publisher · View at Google Scholar · View at Scopus
  34. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Recommender systems for large-scale E-commerce: scable neighborhood formation using clustering,” in Proceedings of the 5th Internation Conference on Computer and Information Technology, 2002.
  35. S. K. Shinde and U. Kulkarni, “Hybrid personalized recommender system using centering-bunching based clustering algorithm,” Expert Systems with Applications, vol. 39, no. 1, pp. 1381–1387, 2012. View at Publisher · View at Google Scholar · View at Scopus