  The Recurrent Chinese Restaurant Process (RCRP) is a powerful statistical
method for modeling evolving clusters in large scale social media data. With
the RCRP, one can allow both the number of clusters and the cluster parameters
in a model to change over time. However, application of the RCRP has largely
been limited due to the non-conjugacy between the cluster evolutionary priors
and the Multinomial likelihood. This non-conjugacy makes inference di cult and
restricts the scalability of models which use the RCRP, leading to the RCRP
being applied only in simple problems, such as those that can be approximated
by a single Gaussian emission. In this paper, we provide a novel solution for
the non-conjugacy issues for the RCRP and an example of how to leverage our
solution for one speci c problem - the social event discovery problem. By
utilizing Sequential Monte Carlo methods in inference, our approach can be
massively paralleled and is highly scalable, to the extent it can work on tens
of millions of documents. We are able to generate high quality topical and
location distributions of the clusters that can be directly interpreted as real
social events, and our experimental results suggest that the approaches
proposed achieve much better predictive performance than techniques reported in
prior work. We also demonstrate how the techniques we develop can be used in a
much more general ways toward similar problems.
