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 ...

Jul 25, 2019 · Our goal is to balance the user's interest in finding relevant content with the provider's interest in protecting sensitive content. We propose ...

Jointly Modeling Relevance and Sensitivity for. Search Among Sensitive Content. Mahmoud F. Sayed, Douglas W. Oard. Page 2. Image credit: HITEC Dubai. 2. Page 3 ...

The aim is to reduce the number of documents that need to be manually reviewed, i.e., the reviewing effort, to train a sensitivity classifier. G. McDonald, C.

Jul 18, 2019 · The understanding of the process of relevance judgment helps to inspire the design of retrieval models. Traditional retrieval models usually ...

This will require developing a new class of evaluation measures that are sensitive to both value (relevance) and cost (sensitivity). Factorial vignette survey ...

Sayed and Douglas W. Oard (2019). Jointly Modeling Relevance and Sensitivity for Search Among Sensitive Content. Proceedings of the 42nd International ACM SIGIR ...

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 ...

We argue that a model for the four cell probabilities that determine the joint distribution of screening test result and outcome result is needed.

Jan 10, 2024 · Jointly modeling relevance and sensitivity for search among sensitive content. In Proc of SIGIR. Vaci et al. (2020) ↑ Nemanja Vaci, Qiang ...