Learning to Rank with Small Set of Ground Truth Data

TL;DR

This paper explores ranking techniques for an academic search platform with limited ground truth data, addressing challenges in traditional methods and enhancing user experience in university research environments.

Contribution

It introduces methods for effective ranking with scarce ground truth data, improving academic search tools where conventional approaches are infeasible.

Findings

Enhanced academic search experience for users.

Effective ranking with limited ground truth data.

Improved platform for researchers and students.

Abstract

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.