【摘 要】
：
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 i
【机 构】
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Capital Medical University, China Singapore Manag
【出 处】
：
首都医科大学生物医学工程学科学术年会
论文部分内容阅读
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.
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