Joint Latent Dirichlet Allocation for Social Tags
Abstract of Joint Latent Dirichlet Allocation for Social Tags
Joint Latent Dirichlet Allocation for Social Tags,Social tags, serving as a textual source of simple but useful semantic metadata to reflect the user preference or describe the web objects, has been widely used in many applications. However, social tags have several unique characteristics, i.e., sparseness and data coupling (i.e., non-IIDness), which makes existing text analysis methods such as LDA not directly applicable. In this paper, we propose a new generative algorithm for social tag analysis named joint latent Dirichlet allocation, which models the generation of tags based on both the users and the objects, and thus accounts for the coupling relationships among social tags. The model introduces two latent factors that jointly influence tag generation: the user’s latent interest factor and the object’s latent topic factor, formulated as user-topic distribution matrix and object-topic distribution matrix, respectively. A Gibbs sampling approach is adopted to simultaneously infer the above two matrices as well as a topic-word distribution matrix.
Conclusion
In this paper, we proposed a method for modeling social tags. By considering the non-IID characteristic of social tags, our model introduces two factors, the user interest factor and the object latent topic factor, to jointly affect the generative procedure of tags. As our model utilizes the collaborative information among the users and the objects to extract more explicit information from tags, experiments on four publicly available data sets have demonstrated the advantages of our models compared with other five topic models in terms of PMI scores.
