CPRS: A cloud-based program recommendation system for digital TV platforms

Future Generation Computer Systems

Volumn 27, Issue 6, 2011, Pages 823-835

  Lai, Chin Feng ^a   Chang, Jui Hung ^a   Hu, Chia Cheng ^b   Huang, Yueh Min ^a   Chao, Han Chieh ^c  

^a NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

^b Naval Academy   (Taiwan)

^c NATIONAL ILAN UNIVERSITY   (Taiwan)

Author keywords

Electronic program guide (EPG); k means; k nearest neighbor (kNN); Map reduce

Indexed keywords

COMPUTING POWER; DATA MINING TECHNIQUES; DATA SETS; DIGITAL TV; DIGITAL VIDEO BROADCASTING - TERRESTRIALS; ELECTRONIC PROGRAM GUIDE; K NEAREST NEIGHBOR ALGORITHM; K-MEANS; K-NEAREST NEIGHBOR (KNN); MAP-REDUCE; PREFERENCE DATA; PROGRAM RECOMMENDATION; PROPOSED ARCHITECTURES; SPECIFIC AREAS; TV PROGRAMS;

CLOUD COMPUTING; COMPUTER GRAPHICS; DATA HANDLING; DATA MINING; DIGITAL TELEVISION; MULTIMEDIA SYSTEMS; TELEVISION BROADCASTING;

RECOMMENDER SYSTEMS;

EID: 79953192127     PISSN: 0167739X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.future.2010.10.002     Document Type: Conference Paper

References (32)

1. P. Johan, V.S. Michiel, W. Jun, J.T.R. Marcel, S. Henk, P2P-based PVR recommendation using friends, taste buddies and superpeers, in: Proceedings of Beyond Personalization 2005, San Diego, 2005, pp. 6671.
2. ETSI EN 300 468 V1.7.1, Digital Video Broadcasting (DVB); Specification for Service Information (SI) in DVB System, 2005.
3. B. Patrick, and B. Lars TV scout: lowering the entry barrier to personalized TV program recommendation Lecture Notes in Computer Science 2005 299 309
4. C. Paul, S. Barry, PTV: intelligent personalised TV guides, in: Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence, 2000, pp. 957964.
5. E. Michael, H. CenterTheo, W. Hartmut, S. GmbHRalf, The personal electronic program guidetowards the pre-selection of individual TV programs, in: Proceedings of the Fifth International Conference on Information and Knowledge Management, 1996 pp. 243250.
6. F. Jacques, Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence, Boston, MA, USA, 1999.
7. Y. Zhiwen, and Z. Xingshe TV3P: an adaptive assistant for personalized TV IEEE Transactions on Consumer Electronics 50 2004 393 399
8. I. Tadashi, F. Masao, K. Hiroyuki, M. Toshiya, and U. Noriyoshi Development of a TV reception navigation system personalized with Viewing Habits IEEE Transactions on Consumer Electronics 51 2005 665 674
9. B.V. Ginneken, A. mendrik, Image denoising with k-nearest neighbor and support vector regression, in: Proceedings of the 18th International Conference on Pattern Recognition, vol. 3, 2006, pp. 603606. (Pubitemid 46553194)
10. Sudha Velusamy, Lakshmi Gopal, Shalabh Bhatnagar, and Sridhar Varadarajan An efficient ad recommendation system for TV programs Multimedia Systems 2008
11. J. Dean, S. Ghemawat, MapReduce: simplified data processing on large clusters, in: Proceedings of the 6th Conference on Symposium on Operating Systems Design & Implementation, 2004, pp. 137150.
12. Jonathan Cohen Graph twiddling in a MapReduce world Computing in Science and Engineering 11 4 2009 29 41 10.1109/MCSE.2009.120 July/Aug
13. P.K. McKinley, F.A. Samimi, J.K. Shapiro, C. Tang, Service Clouds: a distributed infrastructure for constructing autonomic communication services, in: Proceeding of 2nd IEEE International Symposium on Dependable, Autonomic and Secure Computing, 2006.
14. R.L. Grossman The case for cloud computing IT Professional 11 2 2009 23 27
15. H.E. Schaffer, S.F. Averitt, M.I. Hoit, A. Peeler, E.D. Sills, and M.A. Vouk NCSU's virtual computing lab: a cloud computing solution Computer 42 7 2009 94 97
16. P. Mika, and G. Tummarello Web semantics in the clouds IEEE Intelligent Systems 23 5 2008 82 87
17. Mohammed Al-Zoube E-learning on the cloud International Arab Journal of e-Technology 1 2 2009
18. M. Al Hajj Hassan, M. Bamha, Semi-join computation on distributed file systems using map-reduce-merge model, in: Eighth IEEE International Conference on Data Mining, 2010, pp. 406413.
19. Hung-chih Yang, Ali Dasdan, Ruey-Lung Hsiao, D. Stott Parker, Map-reduce-merge: simplified relational data processing on large 7clusters, SIGMOD: ACM Special Interest Group on Management of Data, 2007, pp. 10291040. (Pubitemid 47630879)
20. R.J. Kuo, L.M. Ho, and C.M. Hu Integration of self-organizing feature map and K-means algorithm for market segmentation Computers and Operations Research 29 2002 1475 1493 (Pubitemid 34573192)
21. K.J. Kim, and H. Ahn A recommender system using GA K-means clustering in an online shopping market International Journal of Expert Systems with Applications 34 2008 1200 1209 (Pubitemid 350117489)
22. Vo Dinh Minh Nhat, SungYoung Lee, k-means discriminant maps for data visualization and classification, in: Proceedings of the 2008 ACM symposium on Applied computing, 2008, pp. 11871191.
23. Fred, ALN, and A.K. Jain Combining multiple clusterings using evidence accumulation IEEE Transactions on Pattern Analysis and Machine Intelligence 2005
24. F. Camastra, and A. Verri A novel kernel method for clustering IEEE Transactions on Pattern Analysis and Machine Intelligence 2005
25. J.T. Leonard, and K. Roy On selection of training and test sets for the development of predictive QSAR models QSAR & Combinatorial Science 2006
26. E. Alpaydin Introduction to Machine Learning 2004 MIT Press Cambridge, MA
27. B. Li, Q. Lu, and S.W. Yu An adaptive k-nearest neighbor text categorization strategy ACM Transactions on Asian Language Information Processing 3 2004 215 226
28. A. Thomasian, Y. Li, and L.J. Zhang Optimal subspace dimensionality for k-nearest-neighbor queries on clustered and dimensionality reduced datasets with SVD Multimedia Tools and Applications 2008
29. O.W. Kwon, J.H. Lee, Web page classification based on k-nearest neighbor approach, in: Proceedings of the Fifth International Workshop on Information Retrieval with Asian Languages, 2000, pp. 915.
30. Y.I. Qi, M.J. Atallah, Efficient privacy-preserving k-nearest neighbor search, in: The 28th International Conference on Distributed Computing Systems, 2008, pp. 311319.
31. S. Kharsikar, D. Mugler, D. Sheffer, F. Moore, Z.H. Duan, A weighted k-nearest neighbor method for gene ontology based protein function prediction, in: Proceedings of the Second International Multi-Symposiums on Computer and Computational Sciences, 2007, pp. 2531.
32. Y. Zhuang, Y.T. Zhuang, and F. Wu Composite distance transformation for indexing and k-nearest-neighbor searching in high-dimensional spaces Journal of Computer Science and Technology 2007