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  1. NTU Theses and Dissertations Repository
  2. 電機資訊學院
  3. 資訊網路與多媒體研究所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/47402
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dc.contributor.advisor周承復
dc.contributor.authorChia-Ying Chienen
dc.contributor.author簡嘉瑩zh_TW
dc.date.accessioned2021-06-15T05:58:14Z-
dc.date.available2013-08-20
dc.date.copyright2010-08-20
dc.date.issued2010
dc.date.submitted2010-08-16
dc.identifier.citation[1] IMDb. http://www.imdb.com/.
[2] Movielens. http://movielens.umn.edu/login.
[3] Netflix. http://www.netflixprize.com/.
[4] G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems:
A survey of the state-of-the-art and possible extensions. IEEE Trans. on
Knowl. and Data Eng., 17(6):734–749, 2005.
[5] E. Aimeur, G. Brassard, J. M. Fernandez, and F. S. Mani Onana. Alambic: a
privacy-preserving recommender system for electronic commerce. Int. J. Inf. Secur.,
7(5):307–334, 2008.
[6] M. Balabanovi’c and Y. Shoham. Fab: content-based, collaborative recommendation.
Commun. ACM, 40(3):66–72, 1997.
[7] C. Basu, H. Hirsh, and W. Cohen. Recommendation as classification: Using social
and content-based information in recommendation. In In Proceedings of the Fifteenth
National Conference on Artificial Intelligence, pages 714–720. AAAI Press,
1998.
[8] R. M. Bell and Y. Koren. Improved neighborhood-based collaborative filtering.
[9] S. Debnath, N. Ganguly, and P. Mitra. Feature weighting in content based recommendation
system using social network analysis. In WWW ’08: Proceeding of the
17th international conference on World Wide Web, pages 1041–1042, New York,
NY, USA, 2008. ACM.
[10] J. Douceur, J. Mickens, T. Moscibroda, and D. Panigrahi. Collaborative measurements
of upload speeds in p2p systems. In INFOCOM, 2010 Proceedings IEEE,
pages 1 –9, 14-19 2010.
[11] D. Goldberg, D. Nichols, B. M. Oki, and D. Terry. Using collaborative filtering to
weave an information tapestry. Commun. ACM, 35(12):61–70, 1992.
[12] N. Hu, S. Member, P. Steenkiste, and S. Member. Evaluation and characterization of
available bandwidth probing techniques. IEEE Journal on Selected Areas in Communications,
21:879–894, 2003.
[13] M. Jain and C. Dovrolis. Pathload: A measurement tool for end-to-end available
bandwidth. In In Proceedings of Passive and Active Measurements (PAM) Workshop,
pages 14–25, 2002.
[14] M. Jamali and M. Ester. Trustwalker: a random walk model for combining trustbased
and item-based recommendation. In KDD ’09: Proceedings of the 15th ACM
SIGKDD international conference on Knowledge discovery and data mining, pages
397–406, New York, NY, USA, 2009. ACM.
[15] M. Jamali and M. Ester. Using a trust network to improve top-n recommendation.
In RecSys ’09: Proceedings of the third ACM conference on Recommender systems,
pages 181–188, New York, NY, USA, 2009. ACM.
[16] C.-C. Kung and C.-F. Chou. On the design of the social-based p2p system for music
recommendation.
[17] C.-J. Lin, Y.-T. Chang, S.-C. Tsai, and C.-F. Chou. Distributed social-based overlay
adaptation for unstructured p2p networks. In IEEE Global Internet Symposium,
2007, pages 1 –6, 11-11 2007.
[18] H. Ma, I. King, and M. R. Lyu. Learning to recommend with social trust ensemble.
In SIGIR ’09: Proceedings of the 32nd international ACM SIGIR conference on
Research and development in information retrieval, pages 203–210, New York, NY,
USA, 2009. ACM.
[19] H. Ma, M. R. Lyu, and I. King. Learning to recommend with trust and distrust
relationships. In RecSys ’09: Proceedings of the third ACM conference on Recommender
systems, pages 189–196, New York, NY, USA, 2009. ACM.
[20] F. McSherry and I. Mironov. Differentially private recommender systems: building
privacy into the net. In KDD ’09: Proceedings of the 15th ACM SIGKDD international
conference on Knowledge discovery and data mining, pages 627–636, New
York, NY, USA, 2009. ACM.
[21] P. Melville, R. J. Mooney, and R. Nagarajan. Content-boosted collaborative filtering
for improved recommendations. In Eighteenth national conference on Artificial intelligence,
pages 187–192, Menlo Park, CA, USA, 2002. American Association for
Artificial Intelligence.
[22] A.-T. Nguyen, N. Denos, and C. Berrut. Improving new user recommendations with
rule-based induction on cold user data. In RecSys ’07: Proceedings of the 2007
ACM conference on Recommender systems, pages 121–128, New York, NY, USA,
2007. ACM.
[23] P. Resnick and H. R. Varian. Recommender systems. Commun. ACM, 40(3):56–58,
1997.
[24] V. J. Ribeiro, R. H. Riedi, R. G. Baraniuk, J. Navratil, and L. Cottrell. pathchirp:
Efficient available bandwidth estimation for network paths, 2003.
[25] S. Saroiu, P. K. Gummadi, and S. D. Gribble. A measurement study of peer-to-peer
file sharing systems. 2002.
[26] D. Stutzbach and R. Rejaie. Understanding churn in peer-to-peer networks. In IMC
’06: Proceedings of the 6th ACM SIGCOMM conference on Internet measurement,
pages 189–202, New York, NY, USA, 2006. ACM.
[27] J. Travers, S. Milgram, J. Travers, and S. Milgram. An experimental study of the
small world problem. Sociometry, 32:425–443, 1969.
[28] J. Wang, A. P. de Vries, and M. J. T. Reinders. Unifying user-based and item-based
collaborative filtering approaches by similarity fusion. In SIGIR ’06: Proceedings of
the 29th annual international ACM SIGIR conference on Research and development
in information retrieval, pages 501–508, New York, NY, USA, 2006. ACM.
[29] D. J. Watts and S. H. Strogatz. Collective dynamics of ’small-world’ networks.
Nature, 393(6684):440–442, June 1998.
[30] H. Yildirim and M. S. Krishnamoorthy. A random walk method for alleviating the
sparsity problem in collaborative filtering. In RecSys ’08: Proceedings of the 2008
ACM conference on Recommender systems, pages 131–138, New York, NY, USA,
2008. ACM.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/47402-
dc.description.abstract推薦系統向來是個熱門的焦點,目前已經在許多平台上普遍的被使用,例如像電子商務這種龐大的系統,推薦機制可以幫助使用者更快速找到自己有興趣的項目。此外,在這個資訊流通的時代,點對點檔案分享系統,一直深受使用者的歡迎。結合兩者,便是一個對使用者而言便利又有效率的系統。對於這樣的應用,過去的推薦系統,並沒有考慮假若使用者去下載系統推薦的項目,是否能夠快速有效的得到檔案。因此在這篇論文中,我們使用了一個分散式的電影搜尋推薦系統,能夠搜尋和分享電影,為了充分利用網路頻寬,減少使用者下載檔案的時間,我們以混合式推薦系統為基礎,再加入網路因素來過濾出網路傳輸品質較好的影片,作為我們最後的推薦項目。最後經由模擬實驗可以看到我們的網路過濾機制,在不影響準確度的情形下,對於檔案傳輸時間確實有所改善。zh_TW
dc.description.abstractRecommendation system is popular and well-known by people, and also used in many platforms, like e-commerce which is large and complex. Recommendation mechanism can help users quickly finding the items that they are interested in. In addition, today, more and more information is transferred in the world, peer-to-peer file sharing system is preferred by users. For all the users, it is a strong and convenient service to combine these two ideas. For this kind of applications, existed recommendation systems didn’t consider if users could get the files as quick as possible. Therefore, in this thesis, we propose a distributed movie search recommendation system which can search and share movies. In order to use users’ bandwidth adequately and decrease the time for users to download the files, we base on a hybrid recommendation systems, and consider network factors to filter out the movies which can be downloaded efficiently in network as our final recommendation list. Finally,according to the simulation result , it shows that our network filter can help decreasing the time to transfer files without effecting the accuracy.en
dc.description.provenanceMade available in DSpace on 2021-06-15T05:58:14Z (GMT). No. of bitstreams: 1
ntu-99-R97944045-1.pdf: 1876831 bytes, checksum: d5091d18c52f9e8c05b4173a8c8e9729 (MD5)
Previous issue date: 2010
en
dc.description.tableofcontents致謝ii
中文摘要iii
Abstract iv
1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Challenge and Our Idea . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Related Works 4
3 System Framework 7
3.1 Search and Recommendation Procedure . . . . . . . . . . . . . . . . . . 7
3.2 Social-based Overlay Construction . . . . . . . . . . . . . . . . . . . . . 9
3.3 Hybrid Recommendation System . . . . . . . . . . . . . . . . . . . . . . 10
3.4 Network Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.4.1 On-line Time Estimation . . . . . . . . . . . . . . . . . . . . . . 12
3.4.2 Available Upload Bandwidth Estimation . . . . . . . . . . . . . . 13
4 Performance Evaluation 15
4.1 Simulation Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4.2.1 Precision and Recall . . . . . . . . . . . . . . . . . . . . . . . . 16
4.2.2 Download Efficiency . . . . . . . . . . . . . . . . . . . . . . . . 17
5 Conclusion 22
Bibliography 24
dc.language.isozh-TW
dc.title網路感知之以社群理論為基礎之同儕式電影推薦系統zh_TW
dc.titleNetwork-aware Social-based Movie Recommender for Peer-to-Peer Systemen
dc.typeThesis
dc.date.schoolyear98-2
dc.description.degree碩士
dc.contributor.oralexamcommittee廖婉君,王協源,林靖茹,陳昇瑋
dc.subject.keyword分散式推薦系統,電影推薦系統,下載效率,點對點檔案分享系統,可用上傳頻寬,zh_TW
dc.subject.keywordDistributed recommendation system,Movie recommendation system,Download efficiency,P2P file sharing system,Available upload bandwidth,en
dc.relation.page26
dc.rights.note有償授權
dc.date.accepted2010-08-17
dc.contributor.author-college電機資訊學院zh_TW
dc.contributor.author-dept資訊網路與多媒體研究所zh_TW
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