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

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政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 > Item 140.119/113297

請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/113297

題名:	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
作者:	張至偉 Chang, Chih-Wei
貢獻者:	蔡銘峰王釧茹 Tsai, Ming-Feng Wang, Chuan-Ju 張至偉 Chang, Chih-Wei
關鍵詞:	協同過濾分解機器社群影響力分析 Collaborative filtering Factorization machines Social Influence analysis
日期:	2017
上傳時間:	2017-10-02 10:17:01 (UTC+8)
摘要:	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.
參考文獻:	[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.
描述:	碩士國立政治大學資訊科學學系 104753039
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0104753039
資料類型:	thesis
顯示於類別:	[資訊科學系] 學位論文

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