Jointly Modeling Relevance and Sensitivity for Search Among Sensitive Content
Mahmoud F. Sayed, Douglas W. Oard · 2019
Current search engines are designed to find what we want. But unprocessed archival collections can't be made available for search if they contain sensitive content that needs to be protected. Traditionally, content if first examined through a sensitivity review process, which becomes more difficult and time-consuming as content volumes increase. To mitigate these costs and delays, search technology should be capable of providing access to relevant content while protecting sensitive content. This paper proposes an approach that leverages learning to rank techniques. We use learning to rank to optimize a loss function that balances the value of finding relevant content with the imperative to protect sensitive content. In the experiments, a LETOR benchmark dataset, OHSUMED, is used with a subset of the MeSH labels representing the sensitive documents. Results show the efficacy of the proposed approach in comparison with some simpler baselines.