A probabilistic graphical model for topic and preference discovery on social media

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• 作者：Lu Liu ; Feida Zhu ; Lei Zhang ; Shiqiang Yang
• 关键词：Social media mining ; Topic model ; Preference discovery
• 刊名：Neurocomputing
• 出版年：2012
• 出版时间：15 October, 2012
• 年：2012
• 卷：95
• 期：Complete
• 页码：78-88
• 全文大小：713 K

文摘

Many web applications today thrive on offering services for large-scale multimedia data, e.g., Flickr for photos and YouTube for videos. However, these data, while rich in content, are usually sparse in textual descriptive information. For example, a video clip is often associated with only a few tags. Moreover, the textual descriptions are often overly specific to the video content. Such characteristics make it very challenging to discover topics at a satisfactory granularity on this kind of data. In this paper, we propose a generative probabilistic model named Preference-Topic Model (PTM) to introduce the dimension of user preferences to enhance the insufficient textual information. PTM is a unified framework to combine the tasks of user preference discovery and document topic mining together. Through modeling user-document interactions, PTM cannot only discover topics and preferences simultaneously, but also enable them to inform and benefit each other in a unified framework. As a result, PTM can extract better topics and preferences from sparse data. The experimental results on real-life video application data show that PTM is superior to LDA in discovering informative topics and preferences in terms of clustering-based evaluations. Furthermore, the experimental results on DBLP data demonstrate that PTM is a general model which can be applied to other kinds of user-document interactions.