Name
Abstract | ||
|---|---|---|
Topic models have been widely used for analyzing text corpora and achieved great success in applications including content organization and information retrieval. However, different from traditional text data, social tags in the web containers are usually of small amounts, unordered, and non-iid, i.e., it is highly dependent on contextual information such as users and objects. Considering the specific characteristics of social tags, we here introduce a new model named Joint Latent Dirichlet Allocation (JLDA) to capture the relationships among users, objects, and tags. The model assumes that the latent topics of users and those of objects jointly influence the generation of tags. The latent distributions is then inferred with Gihhs sampling. Experiments on two social tag data sets have demonstrated that the model achieves a lower predictive error and generates more reasonable topics. We also present an interesting application of this model to object recommendation. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA & EXPO (ICME) | Topic model, social tags, JLDA, non-iid learning |
Field | DocType | ISSN |
Data mining,Data set,Latent Dirichlet allocation,Contextual information,Information retrieval,Computer science,Content organization,Text corpus,Topic model,Social tags,Gibbs sampling | Conference | 1945-7871 |
Citations | PageRank | References |
1 | 0.35 | 8 |
Authors | ||
6 | ||