  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.
