在資訊時代，人們即使在閒暇之餘仍有大量的資訊需要處理。例如當我們打開電視，大量可供選擇的電視頻道讓觀眾難以選擇他們想要看的。因此本論文提出了一個人化數位電視節目推薦系統，讓使用者可以藉由本推薦系統瞭解哪些節目是熱門及目前有哪些適合觀看的新節目
本研究主要的架構第一部份提到本系統如何利用電視節目指南(EPG) 應用P2P社群網路方便的技術取得使用者觀看節目資訊，第二部份利用EPG建置一個虛擬的節目觀看平台，產生大量的使用者觀看節目資訊，並利用雲端運算的技術處理使用者產生的大量的資料，最後提到使用了雲端公平排程架構來確保系統的拓展性。
而在節目推薦演算法方面，我們使用了k-means遞迴分群演算法；為了尋找相同興趣的節目群組，則使用了Tf-Idf(詞頻/逆向文件頻率)演算法瞭解該群組的熱門節目，最後利用 kNN (第k位最接近的鄰居) 進行推薦。
大多數的電視節目推薦系統研究主題主要焦點集中提供一個個人的推薦系統。本篇論文的研究則同時考慮了使用者與群組的關係和大多數人看電視節目的偏好。提出一個新的方式提供使用者節目選擇的選項，並利用雲端運算的技術處理大量的使用者行為資訊以取得使用者的節目推薦結果。最後，本研究也對系統效能的進行實驗, 瞭解本系統架構的效能是可行的。

In the era of information technology, people still have to digest a large amount of information in spare time. For example, viewers is too hard to choose which TV program they want to watch. This study presents a personal TV program recommendation system so that the viewers are able to find TV programs which they are more likely to enjoy.
The recommendation system uses the Electronic Program Guide (EPG) to acquire details of programs watched by viewers through a P2P social network. The EPG is also used to establish a virtual program watching platform that can obtain a large amount of information about what programs users watch., and thus data is then processed using cloud computing. The Fair Scheduler cloud-based architecture used in this system means that it is very scalable.
The K-means recursive clustering algorithm is used in the TV program recommendation system. The Term Frequency/Inverse Document Frequency (Tf/Idf) algorithm is used to find out the popular programs in clusters in order to find users with the same interest. The system uses the k-nearest neighbor (kNN) algorithm to process the recommendations.
Most studies of TV program recommendation system focus on recommendation system for individual people. In contrast, this work considers the relationship between users and groups, with a focus on the most popular programs. The system applies cloud-computing to handle large amounts of user behavior data, and to obtain program recommendations. The performance of this system is examined, and the results show that its architecture is very efficient.

[1] Z. Yu, X. Zhou, Y. Hao and J. Gu, "TV Program Recommendation for Multiple Viewers Based on User Profile Merging," User Modeling and User-Adapted Interaction, Volume 16, Number 1, pp. 63-82, 2006.
[2] T. Isohe, M. Fujiwara, H. Kaneta, N. Uratani and T. Morita, "Development and Features of a TV navigation System," IEEE Transactions on Consumer Electronics, pp. 82-83, 2003.
[3] J. Xu, J. L. Zhang, H. Lu and Y. Li, "The Development and Prospect of Personalized TV Program Recommendation Systems," in Fourth International Symposium on Multimedia Software Engineering, 2002.
[4] D. Goren-Bar and O. Glinansky, "FIT-recommending TV Programs to Family Members," Computers and Graphics, Volume. 28, pp. 149-156, 2004.
[5] T. Tsunoda and M. Hoshino, "Automatic Metadata Expansion and Indirect Collaborative Filtering for TV Program Recommendation System," Multimedia Tools and Applications, Volume 36, Issue 1-2, pp. 37-54, 2008.
[6] H. Zhang, S. Zheng and J. Yuan, "A Personalized TV Guide System Compliant with MHP," IEEE Transactions on Consumer Electronics, pp. 731-737, 2005.
[7] A. Buczak, J. Zimmerman and K. Kurapati, "Personalization: Improving Ease-of-Use, Trust and Accuracy of a TV Show Recommender," in Proceedings of the AH2002 Workshop on Personalization in Future TV, 2002.
[8] S. H. Hsu, H. M. Wen, C. H. Lin, C. C. Lee and H. C. Lee, "AIMED- A Personalized TV Recommendation System," Interactive TV: a Shared Experience, Lecture Notes in Computer Science, Volume 4471, pp. 166-174, 2007.
[9] P. Resnick and H. R. Vairan, "Recommender Systems," Communications of the ACM, Volume 40, Issue 3, pp. 56-58, 1997.
[10] J. V. Barneveld and M. V. Setten, "Designing Usable Interfaces for TV Recommender Systems," Personalized Digital Television, Human-Computer Interaction Series, Volume 6, pp. 259-285, 2004.
[11] Z. Yu, X. Zhou and Z. Yang, "A Hybrid Learning Approach for TV Program Personalization," Knowledge-Based Intelligent Information and Engineering Systems, Lecture Notes in Computer Science, Volume 3213, pp. 630-636, 2004.
[12] J. Masthoff, "Group modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers," User Modeling and User-Adapted Interaction, Volume 14, Issue 1, pp. 37-85, 2004.
[13] B. Smith, P. Cotter and G. O'Hare, "Let’s Get Personal: Personalised Television Listings on the Web," in 9th Irish Conference on Artificial Intelligence and Cognitive Science, 1998.
[14] Z. Yu, "TV3P: AnAdaptive Assistant for Personalized TV," IEEE Transactions on Consumer Electronics, Volume 50, Issue 1, pp. 393-399, 2004.
[15] Y. Blanco-Fernández, J. J. Pazos-Arias, A. Gil-Solla and M. Ramos-Cabrer, "A Multi-Agent Open Architecture for a TV Recommender System: A Case Study Using a Bayesian Strategy," in IEEE Sixth International Symposium on Multimedia Software Engineering, 2004.
[16] K. Kurapati and S. Gutta, "Instant Personalization via Clustering TV Viewing Patterns," in 6th International Conference Artificial Intelligence and Soft Computing (IASTED), 2002.
[17] A. Pigeau, G. Raschia, N. Mouaddib and R. Saint-paul, "A Fuzzy Linguistic Summarization Technique for TV Recommender Systems," in The 12th IEEE International Conference on Fuzzy Systems, 2003.
[18] R. Burke, "Hybrid Recommender Systems: Survey and Experiments," User Modeling and User-Adapted Interaction, Volume 12, Issue 4, pp. 331-370, 2002.
[19] P. Baudisch and L. Brueckner, "TV Scout: Lowering the Entry Barrier to Personalized TV Program Recommendation," Integrated Publication and Information Systems to Information and Knowledge Environments, Lecture Notes in Computer Science, Volume 3379, pp. 299-309, 2005.
[20] J. Dean and S. Ghemawat, "MapReduce: Simplified Data Processing on Large Clusters," in OSDI'04: Sixth Symposium on Operating System Design and Implementation, 2004.
[21] C. H. Yang, A. Dasdan, L. R. Hsiao and S. D. Parker, "Map-reduce-merge: Simplified Relational Data Processing on Large 7 Clusters," in The ACM SIGMOD International Conference on Management of Data, 2007.
[22] P. K. McKinley, F. A. Samimi and J. K. Shapiro, "Service Clouds: a Distributed Infrastructure for Constructing Autonomic Communication Services," in 2nd IEEE International Symposium on Dependable, Autonomic and Secure Computing, 2006.
[23] J. Cohen, "Graph Twiddling in a MapReduce World," Computing in Science & Engineering, Volume 11, Issue 4, pp. 29-41.
[24] R. L. Grossman, "The Case for Cloud Computing," IT Professional, Volume 11, Issue 2, pp. 23-27, 2009.
[25] 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, Volume 42, Issue 7, pp. 94-97, 2009.
[26] P. Mika and G. Tummarello, "Web Semantics in the Clouds," IEEE Intelligent Systems, Volume 23, Issue 5, pp. 82-87, 2008.
[27] M. Al-Zoube, "E-Learning on the Cloud," International Arab Journal of e-Technology, Vol. 1, No. 2, pp. 58-64, 2009.
[28] M. A. H. Hassan and M. Bamha, "Semi-join Computation on Distributed File Systems Using Map-reduce-merge Model," in Proceedings of the 2010 ACM Symposium on Applied Computing, 2010.
[29] J. Xie, S. Yin, X. Ruan, Z. Ding, Y. Tian, J. Majors, A. Manzanares and X. Qin, "Improving MapReduce Performance Through Data Placement in Heterogeneous Hadoop Clusters," in IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum, 2010.
[30] C. Tian, H. Zhou, Y. He and L. Zha, "A Dynamic MapReduce Scheduler for Heterogeneous Workloads," in 8th International Conference on Grid and Cooperative Computing, 2009.
[31] B. Panda, J. S. Herbach, S. Basu and R. J. Bayardo, "PLANET: Massively Parallel Learning of Tree Ensembles with MapReduce," Proceedings of the VLDB Endowment, Volume 2, Issue 2, pp. 1426-1437, 2009.
[32] T. Sandholm and K. Lai, "Dynamic Proportional Share Scheduling in Hadoop," Job Scheduling Strategies for Parallel Processing, Lecture Notes in Computer Science, Volume 6253, pp. 100-131, 2010.
[33] G. Lee, N. Tolia, P. Ranganathan and R. H. Katz, "Topology-aware Resource Allocation for Data-intensive Workloads," in Proceedings of the first ACM asia-pacific workshop on Workshop on systems, 2010.
[34] Q. Chen, D. Zhang, M. Guo, Q. Deng and S. Guo, "SAMR: A Self-adaptive MapReduce Scheduling Algorithm in Heterogeneous Environment," in IEEE 10th International Conference on Computer and Informatioin Technology, 2010.
[35] W. Hu, C. Tian, X. Liu, H. Qi, L. Zha, H. Liao, Y. Zhang and J. Zhang, "Multiple-Job Optimization in MapReduce for Heterogeneous Workloads," in Sixth International Conference on Semantics Knowledge and Grid, 2010.
[36] "How to complie Hadoop's scheduler?," 2010. [Online]. Available: http://forum.hadoop.tw/viewtopic.php?f=7&t=19.
[37] "The Hadoop Fair Scheduler," 2009. [Online]. Available: http://www.docstoc.com/docs/18168195/The-Hadoop-Fair-Scheduler.
[38] "The settings about Fair Scheduler," 2010. [Online]. Available: http://forum.hadoop.tw/viewtopic.php?f=4&t=60.
[39] "The performance effect on every node," 2010. [Online]. Available: http://hadoop.nchc.org.tw/phpbb/viewtopic.php?f=4&t=77.
[40] J. A. Xu and K. Araki, "A Personalized Recommendation System for Electronic Program Guide," AI 2005: Advances in Artificial Intelligence, Lecture Notes in Computer Science, Volume 3809, pp. 1146-1149, 2005.
[41] M. Ehrmantraut, T. Härder, H. Wittig and R. Steinmetz, "The Personal Electronic Program Guide - Towards the Pre-selection of Individual TV Programs," in Proceedings of the Fifth International Conference on Information and Knowledge Management, 1996.
[42] M. Ko and I. Koo, "An Overview of Interactive Video On Demand System", The University of British Columbia, 1996.
[43] V. D. M. Nhat and S. Lee, "k-Means Discriminant Maps for Data Visualization and Classification," in Proceedings of the ACM Symposium on Applied Computing, 2008.
[44] J. L. Marroquin and F. Girosi, "Some Extensions of the K-Means Algorithm for Image Segmentation and Pattern Classification," Technical Report: AIM-1390, 1993.
[45] B. Li, Q. Lu and S. Yu, "An Adaptive k-Nearest Neighbor Text Categorization Strategy," ACM Transactions on Asian Language Information Processing, Volume 3, Issue 4, pp. 215-226, 2004.
[46] O. W. Kwon and J. K. Lee, "Text Categorization Based on k-Nearest Neighbor Approach for Web Site Classification," Information Processing & Management, Volume 39, Issue 1, pp. 25-44, 2003.
[47] O. W. Kwon and 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.
[48] R. J. Kuo, L. M. Ho and C. M. Hu, "Integration of Self-organizing Feature Map and K-means Algorithm for Market Segmentation," Computers & Operations Research, Volume 29, Issue 11, pp. 1475-1493, 2002.
[49] K. Kim and H. Ahn, "A Recommender System Using GA K-means Clustering in an Online Shopping Market," Expert Systems with Applications, Volume 34, Issue 2, pp. 1200-1209, 2008.
[50] S. Kharsikar, D. Mugler, D. Sheffer, F. Moore and Z. H. Duan, "A Weighted k-Nearest Neighbor Method for Gene Ontology Based Protein Function Prediction," in Second International Multi-Symposiums on Computer and Computational Sciences, 2007.
[51] G. Jagannathan and R. N. Wright, "Privacy-preserving Distributed K-means Clustering Over Arbitrarily Partitioned Data," in Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, 2005.
[52] E. H. Han, G. Karypis and V. Kumar, "Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification," Advances in Knowledge Discovery and Data Mining, Lecture Notes in Computer Science, Volume 2035, pp. 53-65, 2001.
[53] B. V. Ginneken and A. Mendrik, "Image Denoising with k-nearest Neighbor and Support Vector Regression," in 18th International Conference on Pattern Recognition, 2006.
[54] R. O. Duda, P. E. Hart and D. G. Stork, "Pattern Classification (2nd Edition)", Wiley, 2001.
[55] E. Alpaydin, "Introduction to Machine Learning", The MIT Press, 2004.