政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/113297

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/113297

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | Items with full text/Total items : 113318/144297 (79%)
Visitors : 51075624 Online Users : 948

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

Scope

please add "double quotation mark" for query phrases to get precise results

please goto advance search for comprehansive author search

Adv. Search

Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer

Goto mobile version

政大典藏 > College of Informatics > Department of Computer Science > Theses > Item 140.119/113297

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/113297

Title:	基於分解機器之社群影響力分析研究－以GitHub為例 Social influence analysis based on factorization machines : using GitHub as an example
Authors:	張至偉 Chang, Chih-Wei
Contributors:	蔡銘峰王釧茹 Tsai, Ming-Feng Wang, Chuan-Ju 張至偉 Chang, Chih-Wei
Keywords:	協同過濾分解機器社群影響力分析 Collaborative filtering Factorization machines Social Influence analysis
Date:	2017
Issue Date:	2017-10-02 10:17:01 (UTC+8)
Abstract:	社群協同合作平台的出現創造了一種嶄新的合作形式，可以讓人們或是團體在協同合作的平台上，以實現共同目標為目的，進行用戶之間的互動和開發。以 GitHub 軟體協同合作平台為例，在專案開發的合作過程中，此平台記錄了所有參與使用者的互動過程，這些互動過程紀錄著使用者對於專案貢獻的程度之外，也隱含著使用者彼此之間的影響能力。本文藉討論與衡量影響力來排名過程中的貢獻者，以促進用戶之間更緊密的合作關係。傳統的基於圖形的方法在處理此類問題時，會因為圖形表示法的侷限，難以把用戶和協同合作專案的額外訊息或後設資料（ Metadata ）整合到建立模型的過程中，無法完整闡述用戶在合作的專案之間的互動過程。因此，本研究提出一種利用推薦模型，來整合用戶與合作專案的互動過程，並在學習的過程中加入用戶與專案程式碼中的 API 資訊，來模擬整個協同合作過程中的影響力擴散傳遞的情形。透過此模型，本論文提出的方法將可以測量每個用戶對協同合作專案的潛在影響力值，進而衡量出每一用戶對於整個社群影響力，以從 GitHub 蒐集的真實數據集上進行實驗，證明本研究所提出方法之有效性，在基於一些網路上提供的排名基準，本論文提出的方法可以提供更好的影響力排名結果。除此之外，以視覺化的方式呈現實驗的結果，從中觀察出程式碼的 API 資訊對於量化 GitHub 的社群影響力的重要性。 The emergence of community collaboration platform creates a new form of cooperation that allows people or groups to interact and develop a project with users for the purpose of achieving common goals. Taking GitHub, a collaboration platform, as an example, this platform records the detailed interaction of all participating users in the process of project development. This paper aims to discuss and measure the influence of the contributors using their interactions and the additional information of projects on the platform. In specific, this study proposes a framework to integrate the interaction between users and collaborative projects and, in the process, to learn to merge the user and the project code in the API information so as to simulate the entire process of cooperation under the impact of the proliferation of transmission of user influence. The proposed method is able to measure the potential impact of each user on collaborative projects and thus the impact of each user on the entire community of GitHub collected from the real dataset in the experiments. The experimental results show that the proposed method provides better ranking results than several baseline methods. In addition, this thesis provides a visualization of the experimental results.
Reference:	[1] S. Brin and L. Page. The anatomy of a large-scale hypertextual web search engine. In Proceedings of the 7th International World Wide Web Conference, pages 107–117, 1998. [2] M. Cha, A. Mislove, and K. P. Gummadi. A measurement-driven analysis of information propagation in the flickr social network. In Proceedings of the 18th International Conference on World Wide Web, pages 721–730. ACM, 2009. [3] D. Gruhl, R. Guha, D. Liben-Nowell, and A. Tomkins. Information diffusion through blogspace. In Proceedings of the 13th International Conference on World Wide Web, pages 491–501. ACM, 2004. [4] L. Hong, O. Dan, and B. D. Davison. Predicting popular messages in twitter. In Proceedings of the 20th International Conference Companion on World Wide Web, pages 57–58. ACM, 2011. [5] M. G. Kendall. A new measure of rank correlation. Biometrika, 30(1/2):81–93, 1938. [6] N. Li and D. Gillet. Identifying influential scholars in academic social media platforms. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pages 608–614. ACM, 2013. [7] A. Lima, L. Rossi, and M. Musolesi. Coding together at scale: Github as a collaborative social network. arXiv preprint arXiv:1407.2535, 2014. [8] L. Liu, J. Tang, J. Han, and S. Yang. Learning influence from heterogeneous social networks. Data Mining and Knowledge Discovery, 25(3):511–544, 2012. [9] X. Liu, J. Bollen, M. L. Nelson, and H. Van de Sompel. Co-authorship networks in the digital library research community. Information Processing & Management, 41(6):1462–1480, 2005. [10] P. Mutschke. Mining networks and central entities in digital libraries. a graph theoretic approach applied to co-author networks. In International Symposium on Intelligent Data Analysis, pages 155–166. Springer, 2003. [11] J. L. Myers, A. Well, and R. F. Lorch. Research design and statistical analysis. Routledge, 2010. [12] S. A. Myers, C. Zhu, and J. Leskovec. Information diffusion and external influence in networks. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 33–41. ACM, 2012. [13] S. Rendle. Factorization machines with libfm. ACM Transactions on Intelligent Systems and Technology (TIST), 3(3):57, 2012. [14] X. Shuai, Y. Ding, J. Busemeyer, S. Chen, Y. Sun, and J. Tang. Modeling indirect influence on twitter. International Journal on Semantic Web and Information Systems (IJSWIS), 8(4):20–36, 2012. [15] L. Terveen and W. Hill. Beyond recommender systems: Helping people help each other. HCI in the New Millennium, 1(2001):487–509, 2001. [16] M.-F. Tsai, C.-J. Wang, and Z.-L. Lin. Social Influencer Analysis with Factorization Machines. In Proceedings of the ACM Web Science Conference, WebSci ’15, pages 50:1–50:2, New York, NY, USA, 2015. ACM. [17] S. White and P. Smyth. Algorithms for estimating relative importance in networks. In Proceedings of the ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 266–275. ACM, 2003. [18] E. Yan and Y. Ding. Discovering author impact: A pagerank perspective. Information Processing & Management, 47(1):125–134, 2011. [19] L.-c. Yin, H. Kretschmer, R. A. Hanneman, and Z.-y. Liu. Connection and stratification in research collaboration: An analysis of the collnet network. Information Processing & Management, 42(6):1599–1613, 2006.
Description:	碩士國立政治大學資訊科學學系 104753039
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0104753039
Data Type:	thesis
Appears in Collections:	[Department of Computer Science ] Theses

Files in This Item:

File	Description	Size	Format
303901.pdf		2546Kb	Adobe PDF2	221	View/Open

All items in 政大典藏 are protected by copyright, with all rights reserved.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - Feedback