Latent Subject-Centered Modeling of Collaborative Tagging: An Application in Social Search
Jing Peng, Daniel Dajun Zeng, Zan Huang · SSRN Electronic Journal · 2011
Collaborative tagging or social bookmarking is a main component of Web 2.0 systems and has been widely recognized as one of the key technologies underpinning next-generation knowledge management platforms. In this paper, we propose a subject-centered model of collaborative tagging to account for the ternary co-occurrences involving users, items, and tags in such systems. Extending the well-established probabilistic latent semantic analysis theory for knowledge representation, our model maps the user, item, and tag entities into a common latent subject space that captures the, “wisdom of the crowd,” resulted from the collaborative tagging process. To put this model into action, we have developed a novel way to estimate the probabilistic subject-centered model approximately in a highly efficient manner taking advantage of a matrix factorization method. Our empirical evaluation shows that our proposed approach delivers substantial performance improvement on the knowledge resource recommendation task over the state-of-the-art standard and tag-aware resource recommendation algorithms.