Clustering via Content-Augmented Stochastic Blockmodels

TL;DR

This paper introduces content-augmented stochastic blockmodels (CASB) that integrate item content with interaction data to improve clustering accuracy in bipartite graphs, outperforming existing methods.

Contribution

The paper proposes CASB, a novel model combining content and interaction data for better community detection in bipartite graphs.

Findings

CASB achieves higher clustering accuracy than benchmark methods.

Content integration significantly improves community detection.

Validated on scientific interaction datasets.

Abstract

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.