Ranking in Genealogy: Search Results Fusion at Ancestry

TL;DR

This paper presents new ranking algorithms and a diversity metric for improving search relevance and diversity in genealogical records at Ancestry, addressing record disparity challenges.

Contribution

It introduces customized coordinate ascent, stochastic search, and normalized cumulative entropy to enhance search result relevance and diversity in genealogical data retrieval.

Findings

Customized CA speeds up ranking within record types.

SS effectively combines results across record types.

Algorithms improve relevance and diversity metrics in experiments.

Abstract

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.