  Genealogy research is the study of family history using available resources
such as historical records. Ancestry provides its customers with one of the
world's largest online genealogical index with billions of records from a wide
range of sources, including vital records such as birth and death certificates,
census records, court and probate records among many others. Search at Ancestry
aims to return relevant records from various record types, allowing our
subscribers to build their family trees, research their family history, and
make meaningful discoveries about their ancestors from diverse perspectives. In
a modern search engine designed for genealogical study, the appropriate ranking
of search results to provide highly relevant information represents a daunting
challenge. In particular, the disparity in historical records makes it
inherently difficult to score records in an equitable fashion. Herein, we
provide an overview of our solutions to overcome such record disparity problems
in the Ancestry search engine. Specifically, we introduce customized coordinate
ascent (customized CA) to speed up ranking within a specific record type. We
then propose stochastic search (SS) that linearly combines ranked results
federated across contents from various record types. Furthermore, we propose a
novel information retrieval metric, normalized cumulative entropy (NCE), to
measure the diversity of results. We demonstrate the effectiveness of these two
algorithms in terms of relevance (by NDCG) and diversity (by NCE) if applicable
in the offline experiments using real customer data at Ancestry.
