  Motivated by the connections between collaborative filtering and network
clustering, we consider a network-based approach to improving rating prediction
in recommender systems. We propose a novel Bipartite Mixed-Membership
Stochastic Block Model ($\mathrm{BM}^2$) with a conjugate prior from the
exponential family. We derive the analytical expression of the model and
introduce a variational Bayesian expectation-maximization algorithm, which is
computationally feasible for approximating the untractable posterior
distribution. We carry out extensive simulations to show that $\mathrm{BM}^2$
provides more accurate inference than standard SBM with the emergence of
outliers. Finally, we apply the proposed model to a MovieLens dataset and find
that it outperforms other competing methods for collaborative filtering.
