We design a recommender system for research papers based on topic-modeling.
The users feedback to the results is used to make the results more relevant the
next time they fire a query. The user's needs are understood by observing the
change in the themes that the user shows a preference for over time.
Related papers
Ranked by semantic similarity — how closely each paper's abstract matches this one (100% = near-identical topic).