| Summary: | 碩士 === 國立臺灣大學 === 資訊工程學研究所 === 100 === 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.
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