There are many general purpose benchmark datasets for Semantic Textual Similarity but none of them are focused on technical concepts found in patents and scientific publications.
Scientific analyses often rely on slow, but accurate forward models for observable data conditioned on known model parameters.
The ranking model with the proposed features results in a $20-30\%$ improvement over the MPC model on all metrics.
We address the problem of personalization in the context of eCommerce search.
We apply this method to eBay search data to estimate click propensities for web and mobile search and compare these with estimates using the EM method.