Probabilistic Graphical Models for Data Mining and Recommendation in Social Media

社交媒体允许创建和交换用户生成的内容，并支持基于Web 2.0技术基础的内容生产者和消费者之间的各种形式的互动。与报纸和电视等传统媒体相比，社交媒体拥有更大和更多样化的生产者群体，导致许多简短和嘈杂的帖子，允许用户评论内容并相互交流，并动态发展。社会媒体有可能为生产者提供有价值的反馈，并使消费者能够利用“群众的智慧”来帮助他们做出决策。我们的研究计划的长期目标是开发（1）数据挖掘方法来模拟社交媒体中集体行动的复杂动态，从而更好地了解这些媒体并预测未来事件。(2)推荐方法，推荐值得信赖和相关的内容，特定于给定的用户，并使社交媒体网站能够提高用户的参与程度。为了对社交媒体中的复杂影响进行建模，我们将探索概率图形模型，这些模型信息量很大，可以自然地整合现有的背景知识。

Social media allow the creation and exchange of user-generated content and support various forms of interactions among content producers and consumers, based on the technological foundations of Web 2.0. Compared to traditional media such as newspapers and TV, social media have a much larger and more diverse group of producers, leading to many short and noisy posts, allow users to comment on content and to communicate with each other, and evolve dynamically. Social media have the potential to provide valuable feedback to producers and to allow consumers to tap into the "wisdom of the crowds" as aid in their decision making. The long-term objectives of our research program are to develop (1) Data mining methods to model the complex dynamics of collective action in social media, enabling a better understanding of these media and the prediction of future events. (2) Recommendation methods that recommend trust-worthy and relevant content, specific to a given user, and enable social media sites to increase the level of user participation. To model the complex effects in social media, we will explore probabilistic graphical models, which are very informative and can naturally integrate available background knowledge.