Probabilistic Graphical Models for Data Mining and Recommendation in Social Media

点击翻译按钮获取中文摘要

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.