  The TREC 2017 Common Core Track aimed at gathering a diverse set of
participating runs and building a new test collection using advanced pooling
methods.
  In this paper, we describe the participation of the IlpsUvA team at the TREC
2017 Common Core Track. We submitted runs created using two methods to the
track: (1) BOIR uses Bayesian optimization to automatically optimize retrieval
model hyperparameters. (2) NVSM is a latent vector space model where
representations of documents and query terms are learned from scratch in an
unsupervised manner.
  We find that BOIR is able to optimize hyperparameters as to find a system
that performs competitively amongst track participants. NVSM provides rankings
that are diverse, as it was amongst the top automated unsupervised runs that
provided the most unique relevant documents.
