  Over the past decades, researchers had put lots of effort investigating
ranking techniques used to rank query results retrieved during information
retrieval, or to rank the recommended products in recommender systems. In this
project, we aim to investigate searching, ranking, as well as recommendation
techniques to help to realize a university academia searching platform. Unlike
the usual information retrieval scenarios where lots of ground truth ranking
data is present, in our case, we have only limited ground truth knowledge
regarding the academia ranking. For instance, given some search queries, we
only know a few researchers who are highly relevant and thus should be ranked
at the top, and for some other search queries, we have no knowledge about which
researcher should be ranked at the top at all. The limited amount of ground
truth data makes some of the conventional ranking techniques and evaluation
metrics become infeasible, and this is a huge challenge we faced during this
project. This project enhances the user's academia searching experience to a
large extent, it helps to achieve an academic searching platform which includes
researchers, publications and fields of study information, which will be
beneficial not only to the university faculties but also to students' research
experiences.
