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Telecommunication Systems
Erschienen in: Telecommunication Systems 1/2015

01.09.2015

A fair scheduler using cloud computing for digital TV program recommendation system

Erschienen in: Telecommunication Systems | Ausgabe 1/2015

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Abstract

With hundreds of TV channels, a good TV program recommendation system can save time. Hadoop fair scheduler cloud computing is designed to make information processing and filtering effective and scalable. In cloud computing, computers are connected over a network and perform computation simultaneously; more computation power can be obtained by adding more computer nodes. In the present study, cloud computing is used to build a TV program recommendation system. A fair scheduler cloud structure is applied to improve the system performance. For program recommendation, the K-means recursive clustering algorithm is used for user clustering, the term frequency/inverse document frequency algorithm is applied for finding related popular programs, and k-nearest neighbor is used to recommend programs. Most TV program recommendation systems focus on providing a personal recommendation system. The proposed system also considers user groups and the program watching preferences of the majority. The proposed fair scheduler cloud-based architecture is scalable; a massive amount of information can be processed in real-time to obtain program recommendation results that can represent almost all users.
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Literatur
1.
Yu, Z., Zhou, X., Hao, Y., & Gu, J. (2006). TV program recommendation for multiple viewers based on user profile merging. User Modeling and User-Adapted Interaction, 16(1), 63–82.
2.
Isobe, T., Fujiwara, M., Kaneta, H., Uratani, N., & Morita, T. (2003). Development and features of a TV navigation system. IEEE Transactions on Consumer Electronics, 49(4), 1035–1042.
3.
Xu, J., Zhang, L. J., Lu, H., & Li, Y. (2002). The development and prospect of personalized TV program recommendation systems. In Proceedings of Multimedia Software Engineering. IEEE
4.
Goren-Bar, D., & Glinansky, O. (2004). FIT-recommend ing TV programs to family members. Computers & Graphics, 28(2), 149–156.
5.
Tsunoda, T., & Hoshino, M. (2006). Automatic metadata expansion and indirect collaborative filtering for TV program recommendation system. Multimedia Tools and Applications, 36(1–2), 37–54.
6.
Zhang, H., Zheng, S., & Yuan, J. (2005). A personalized TV guide system compliant with MHP. IEEE Transactions on Consumer Electronics, 51(2), 731–737.
7.
Buczak, A., Zimmerman, J., & Kurapati, K. (2002). Personalization: improving ease-of-use, trust and accuracy of a TV show recommender, (vol. 239). Human-Computer Interaction Institute
8.
Hsu, S. H., Wen, M.-H., Lin, H.-C., Lee, C.-C., & Lee, C.-H. (2007). AIMED: A personalized TV recommendation system. Interactive TV: A Shared Experience, Lecture Notes in Computer Science, (vol. 4471, pp. 166–174).
9.
Zhou, L., Wang, X., Tu, W., Mutean, G., & Geller, B. (2010). Distributed scheduling scheme for video streaming over multi-channel multi-radio multi-hop wireless networks. IEEE Journal on Selected Areas in Communications, 28(3), 409–419.CrossRef
10.
Zhou, L., & Chen, H.-H. (2011). On distributed multimedia scheduling with constrained control channels. IEEE Transactions on Multimedia, 13(5), 1040–1051.CrossRef
11.
Zhou, L., Chao, H.-C., & Vasilakos, A. (2011). Joint forensics-scheduling strategy for delay-sensitive multimedia applications over heterogeneous networks. IEEE Journal on Selected Areas in Communications, 29(7), 1358–1367.CrossRef
12.
Zhou, L., & Chao, H.-C. (2011). Multimedia traffic security architecture for internet of things. IEEE Network, 25(3), 35–40.CrossRef
13.
de Mello, R. F., Andrade Filho, J. A., Senger, L. J., & Yang, L. T. (2008). Grid job scheduling using route with genetic algorithm support. Telecommunication Systems, 38(3), 147–160.CrossRef
14.
Kurapati, K., & Gutta, S. (2002). Instant personalization via clustering TV viewing patterns. In Proceedings of the 2002 International Association of Science and Technology for Development “Artificial Intelligence and Soft Computing”. Calgary, AB: ACTA Press.
15.
Resnick, P., & Varian, H. R. (1997). Recommender systems. Communications of the ACM, 40(3), 56-58.
16.
Barneveld, J. V., & Setten, M. V. (2004). Designing usable interfaces for TV recommender systems. Human-Computer Interaction Series, 6, 259–286.CrossRef
17.
Yu, Z., Zhou, X., Zhou, Xingshe, & Yang, Z. (2004). A hybrid learning approach for TV program personalization. Knowledge-Based Intelligent Information and Engineering Systems, Lecture Notes in Computer Science, (vol. 3213, pp. 630–636).
18.
Masthoff, J. (2004). Group modeling: Selecting a sequence of television items to suit a group of viewers. User Modeling and User-Adapted Interaction, Computer Science, 14(1), 37–85.CrossRef
19.
Smyth, B., Cotter, P., & O’Hare, G. M. P. (1998). Let’s get personal: Personalised TV listings on the web. In 9th Irish Conference on Artificial Intelligence and Cognitive Science AICS-98, Dublin, Ireland.
20.
Yu, Z. (2004). TV3P: An adaptive assistant for personalized TV. IEEE Transactions on IEEE Consumer Electronics Society, 50(1), 393–399.CrossRef
21.
Fernandez, Y. B., Arias, J. J. P., Solla, A. G., Cabrer, M. R., Martinez, B. B., & Nores, M. L. (2004). A multi-agent open architecture for a TV recommender system: A case study using a Bayesian strategy. In Proceedings of the IEEE Sixth International Symposium on, Multimedia Software Engineering, 178–185. IEEE
22.
Pigeau, A., Raschia, G., Gelgon, M., Mouaddib, N., & Saint-Paul, R. (2003). A fuzzy linguistic summarization technique for TV recommender systems. IEEE International Conference on Fuzzy Systems, 1, 743–748.
23.
Nikolova, E., & Jecheva, V. (2010). Some similarity coefficients and application of data mining techniques to the anomaly-based IDS. Telecommunication Systems, 50, 127–135.
24.
Burke, R. (2002). Hybrid recommender systems. User Modeling and User Adapted Interaction, 12, 331–370.CrossRef
25.
Baudisch, P., & Brueckner, L. (2006). TV scout: Lowering the entry barrier to personalized TV Program recommendation. Adaptive Hypermedia and Adaptive Web-Based Systems, Lecture Notes in Computer Science, Vol. 2347, pp. 58–68.
26.
Al Hajj Hassan, M., & Bamha, M. (2010). Semi-join computation on distributed file systems using map-reduce-merge model. In Proceedings of the Eighth IEEE International Conference on Data Mining (pp. 406–413). ACM.
27.
Yang, H.-C., Dasdan, A., Hsiao, R.-L., & Parker, D. S. (2007). Map-reduce-merge: simplified relational data processing on large 7clusters. In Proceedings of the SIGMOD: ACM Special Interest Group on Management of Data (pp. 1029–1040). ACM.
28.
Pan, J., Magoules, F., Le Biannic, Y., & Favart, C. (2011). Parallelizing multiple group-by queries using MapReduce: Optimization and cost estimation. Telecommunication Systems, 52(2), 635–645.
29.
Xie, J., Yin, Shu., Ruan, X., Ding, Z., Tian, Y., Majors, J., Manzanares, A., & Qin, X. (2010). Improving MapReduce performance through data placement in heterogeneous Hadoop clusters. In Proceedings of the IEEE International Symposium on, Parallel & Distributed Processing, Workshops and Ph.D. Forum (IPDPSW), (pp. 1–9).
30.
Tian, C., Zhou, H., He, Yongqiang, & Zha, L. (2009). A dynamic MapReduce scheduler for heterogeneous workloads. In Proceedings of the Eighth International Conference on Grid and Cooperative Computing, (pp. 218–224). IEEE
31.
Panda, B., Herbach, J. S., Basu, S., & Bayardo, R. J. (2009). PLANET: Massively parallel learning of tree ensembles with MapReduce. In Proceedings of the Eighth International Conference on Grid and Cooperative Computing, (vol. 2, pp. 1426–1437).
32.
Panda, B., Herbach, J. S., Basu, S., & Bayardo, R. J. (2011). Dynamic proportional share scheduling in Hadoop. In Job Scheduling Strategies for Parallel Processing, Lecture Notes in Computer Science, Vol. 6253, pp. 110–131.
33.
Lee, G., Tolia, N., Ranganathan, P., & Katz, R. H. (2010). Topology-aware resource allocation for data-intensive workload. In Proceedings of the IEEE International Conference on Advanced Information Networking and Applications, (pp. 110–131). IEEE
34.
Chen, Q., Zhang, D., Guo, M., Deng, Q., & Guo, S. (2010). SAMR: A self-adaptive MapReduce scheduling algorithm in heterogeneous environment. In Proceedings of the IEEE 10th International Conference on, Computer and Information Technology (CIT), (pp. 2736–2743). IEEE
35.
36.
Lai, C.-F., & Vasilakos, A. V. (2010). Mobile multimedia services over cloud computing. IEEE COMSOC MMTC E-Letter, 5(6).
37.
Hu, W., Tian, C., Liu, X., Qi, H., Zha, L., Liao, H., Zhang, Y., & Zhang, J. (2010). Multiple-job optimization in MapReduce for heterogeneous workloads. In Proceedings of the Semantics Knowledge and Grid (SKG), 2010 Sixth International Conference on, (pp. 135–140). IEEE
38.
39.
40.
41.
42.
Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern classification (2nd ed.). Hoboken, NJ: Wiley.
43.
Alpaydin, E. (2004). Introduction to machine learning. Cambridge, MA: MIT Press.
44.
Kuo, R. J., Ho, L. M., & Hu, C. M. (2002). Integration of self-organizing feature map and K-means algorithm for market segmentation. Computers and Operations Research, 29, 1475–1493.CrossRef
45.
Kim, K. J., & Ahn, H. (2008). A recommender system using GA K-means clustering in an online shopping market. International Journal of Expert Systems with Applications, 34, 1200–1209.CrossRef
46.
Marroquin, J. L., & Girosi, F. (1993). Some extensions of the K-means algorithm for image segmentation and pattern classification. Technical Report, MIT Artificial Intelligence Laboratory.
47.
Jagannathan, G., & Wright, R. N. (2005). Privacy-preserving distributed K-means clustering over arbitrarily partitioned data. In Proceedings of 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining (pp. 593–599). ACM.
48.
Nhat, V. D. M., & Lee, S. (2008). k-means discriminant maps for data visualization and classification. In Proceedings of the 2008 ACM Symposium on Applied computing (pp. 1187-1191). ACM.
Metadaten
Titel
A fair scheduler using cloud computing for digital TV program recommendation system
Publikationsdatum
01.09.2015
Erschienen in
Telecommunication Systems / Ausgabe 1/2015
Print ISSN: 1018-4864
Elektronische ISSN: 1572-9451
DOI
https://doi.org/10.1007/s11235-014-9921-4

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