Abstract As online document collections continue to expand, both on the Web and in proprietary environments, the need for duplicate detection becomes more critical. Few users wish to retrieve search results consisting of sets of duplicate documents, whether identical duplicates or close variants. The goal of this work is to facilitate (a) investigations into the phenomenon of near duplicates and (b) algorithmic approaches to minimizing its deleterious effect on search results. Harnessing the expertise of both client‐users and professional searchers, we establish principled methods to generate a test collection for identifying and handling nonidentical duplicate documents. We subsequently examine a flexible method of characterizing and comparing documents to permit the identification of near duplicates. This method has produced promising results following an extensive evaluation using a production‐based test collection created by domain experts.

As online document collections continue to expand, both on the Web and in proprietary environments, the need for duplicate detection becomes more critical. The goal of this work is to facilitate (a) investigations into the phenomenon of near duplicates and (b) algorithmic approaches to minimizing its negative effect on search results. Harnessing the expertise of both client-users and professional searchers, we establish principled methods to generate a test collection for identifying and handling inexact duplicate documents.

As online document collections continue to expand, both on the Web and in proprietary environments, the need for duplicate detection becomes more critical. Few users wish to retrieve search results consisting of sets of duplicate documents, whether identical duplicates or close matches. Our goal in this work is to investigate the phenomenon and determine one or more approaches that minimize its impact on search results. Recent work has focused on using some form of signature to characterize a document in order to reduce the complexity of document comparisons. A representative technique constructs a 'fingerprint' of the rarest or richest features in a document using collection statistics as criteria for feature selection. One of the challenges of this approach, however, arises from the fact that in production environments, collections of documents are always changing, with new documents, or new versions of documents, arriving frequently, and other documents periodically removed. When an enterprise proceeds to freeze a training collection in order to stabilize the underlying repository of such features and its associated collection statistics, issues of coverage and completeness arise. We show that even with very large training collections possessing extremely high feature correlations before and after updates, underlying fingerprints remain sensitive to subtle changes. We explore alternative solutions that benefit from the development of massive meta-collections made up of sizable components from multiple domains. This technique appears to offer a practical foundation for fingerprint stability. We also consider mechanisms for updating training collections while mitigating signature instability. Our research is divided into three parts. We begin with a study of the distribution of duplicate types in two broad-ranging news collections consisting of approximately 50 million documents. We then examine the utility of document signatures in addressing identical or nearly identical duplicate documents and their sensitivity to collection updates. Finally, we investigate a flexible method of characterizing and comparing documents in order to permit the identification of non-identical duplicates. This method has produced promising results following an extensive evaluation using a production-based test collection created by domain experts.