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Much of the data being created on the web contains interactions between usersand items. Stochastic blockmodels, and other methods for community detectionand clustering of bipartite graphs, can infer latent user communities andlatent item clusters from this interaction data. These methods, however,typically ignore the items' contents and the information they provide aboutitem clusters, despite the tendency of items in the same latent cluster toshare commonalities in content. We introduce content-augmented stochasticblockmodels (CASB), which use item content together with user-item interactiondata to enhance the user communities and item clusters learned. Comparisons toseveral state-of-the-art benchmark methods, on datasets arising from scientistsinteracting with scientific articles, show that content-augmented stochasticblockmodels provide highly accurate clusters with respect to metricsrepresentative of the underlying community structure.

Clustering via Content-Augmented Stochastic Blockmodels