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The TREC 2017 Common Core Track aimed at gathering a diverse set ofparticipating runs and building a new test collection using advanced poolingmethods.  In this paper, we describe the participation of the IlpsUvA team at the TREC2017 Common Core Track. We submitted runs created using two methods to thetrack: (1) BOIR uses Bayesian optimization to automatically optimize retrievalmodel hyperparameters. (2) NVSM is a latent vector space model whererepresentations of documents and query terms are learned from scratch in anunsupervised manner.  We find that BOIR is able to optimize hyperparameters as to find a systemthat performs competitively amongst track participants. NVSM provides rankingsthat are diverse, as it was amongst the top automated unsupervised runs thatprovided the most unique relevant documents.

ILPS at TREC 2017 Common Core Track