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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/16037
標題: | 個人喜好在社群網路中傳播行為的預測 Predicting the Diffusion of Preferences on Social Networks |
作者: | Chin-Hua Tsai 蔡青樺 |
指導教授: | 林守德 |
關鍵字: | 個人喜好傳播行為,社群網路,排序學習, Preference Diffusion,Social Network,Learning-to-Rank, |
出版年 : | 2012 |
學位: | 碩士 |
摘要: | 一直以來,研究喜好如何在社群網路中擴散都是熱門課題。相較於過去往往視喜好為一個實數或單純的布林值,我們改採排名的方式解讀喜好,因而適用許多經典排序學習演算法以解決問題;但現實生活中囊括社群網路、時間以及喜好等完整資訊之數據取得不易,我們為此提出如何從各種數據內,間接提取所需訊息的替代方案。經實驗證明,在不同資料集上,本方法表現皆優於其他傳播模型。 This work tries to bring a marriage between two areas: social network analysis and machine learning, through the study of exploiting ranking-based learning models for preference prediction on social networks. The diffusion of information on social networks has been studied for decades. This paper proposes a study of the diffusion of human preference on social networks, which is a novel problem to solve in this direction. In general, there are two types of approaches proposed to predict the diffusion of information on networks: the model-driven and data-driven approaches. The former assumes an underlying mechanism for diffusion, and the later tries to learn a more flexible model given data. This paper first proposes a simple modification on the existing model-driven binary diffusion approaches for preference list diffusion, and then addresses some concerns by proposing a rank-learning based data-driven approach. To evaluate the approaches, we propose two scenarios which data can be obtained from publicly available sources: the citation behavior and the microblogging behavior changes. The experiments show that the proposed ranking-based data-driven method outperforms all the other competitors significantly in both evaluation scenarios. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/16037 |
全文授權: | 未授權 |
顯示於系所單位: | 資訊工程學系 |
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