Improving Search Ranking for Biomedical Dataset Retrieval with Machine Learning and Relevance Feedback

Leaders: Eugene Agichtein, PhD., and Payam Karisani, Emory University

This project will develop and integrate a Learning-to-Rank (LTR) module into
the BioCADDIE retrieval framework. As there are many possible signals which
can capture the usefulness of a dataset for a user's query, it is difficult
to manually design a ranking function that will perform well for all cases.
Instead, this project will automatically learn a ranking function, or multiple
functions, most effective for the available features, expected queries, and
the supported datasets. The learning-to-rank code, as well as the ranking methods
using the learned models, will be incorporated into the BioCADDIE framework.
Additionally, if time permits, this project will investigate the use of
the searchers' feedback, explicit or implicit, as additional relevance signals,
and will incorporate this feedback as features into the ranking model to enable
the BioCADDIE search to improve over time.