Relative difficulty estimation in community answering services

Abstract

Automatic estimation of relative difficulty of a pair of questions is an important and challenging problem in community question answering (CQA) services. There are limited studies which addressed this problem. Past studies mostly leveraged expertise of users answering the questions and barely considered other properties of CQA services such as metadata of users and posts,temporal information and textual content. In this paper, we propose a system, a novel system that maps this problem to a network-aided edge directionality prediction problem. Given a question on a crowd sourced platform, we gauge the difficulty of the question. We used various graph models in order to model our intuition of how difficulty is associated with questions, the answerers, the asker and how over time the difficulty of one’s questions change