The research overarching goal is to investigate recommendation tasks from a probabilistic perspective. We aim to confront directly with the data uncertainty as part of the recommendation process and to propose new probabilistic ranking techniques for various recommendation tasks. We look for new semantics, evaluation measures and efficient processing methods suitable to various recommendation tasks, towards designing general framework for generating high-quality recommendation.
Goal recognition design is a problem, in which we take a domain theory and a set of goals and ask:
1) to what extent do the actions performed by an agent within the model reveal its objective, and 2) what is the best way to modify a model so that any agent acting in the model reveals its objective early on. As a first stage, Goal Recognition Design finds the Worst Case Distinctiveness (wcd) of a model and as a second stage, after finding the wcd of a model, we aim at minimizing it.
Matching is a task at the heart of any data integration process, aimed at identifying correspondences among data elements. Matching is traditionally solved in a semi-automatic manner, where algorithmic outcomes are validated by human experts. Our research questions the inherent need and usage of humans in the matching loop. The core idea is that matching requires unconventional thinking demonstrated by advance machine learning methods to complement the role of humans in matching.