Title

A Probabilistic Graphical Model for Topic and Preference Discovery on Social Media

Publication Type

Journal Article

Publication Date

10-2012

Abstract

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.

Keywords

Social media mining, Topic model, Preference discovery

Discipline

Digital Communications and Networking

Research Areas

Data Management and Analytics

Publication

Neurocomputing

Volume

95

First Page

78

Last Page

88

ISSN

0925-2312

Identifier

10.1016/j.neucom.2011.05.039

Publisher

Elsevier

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