Clustering via Content-Augmented Stochastic Blockmodels
This addresses the challenge of improving clustering accuracy in bipartite graphs for domains like web data analysis, though it is incremental by augmenting existing methods with content.
The paper tackles the problem of clustering user communities and item clusters from user-item interaction data by incorporating item content, which is typically ignored, and introduces content-augmented stochastic blockmodels (CASB) that show highly accurate clusters compared to state-of-the-art benchmarks on scientific article datasets.
Much of the data being created on the web contains interactions between users and items. Stochastic blockmodels, and other methods for community detection and clustering of bipartite graphs, can infer latent user communities and latent item clusters from this interaction data. These methods, however, typically ignore the items' contents and the information they provide about item clusters, despite the tendency of items in the same latent cluster to share commonalities in content. We introduce content-augmented stochastic blockmodels (CASB), which use item content together with user-item interaction data to enhance the user communities and item clusters learned. Comparisons to several state-of-the-art benchmark methods, on datasets arising from scientists interacting with scientific articles, show that content-augmented stochastic blockmodels provide highly accurate clusters with respect to metrics representative of the underlying community structure.