Assessing the authors of online books in digital libraries using users affinity

Abstract

Information quality generated by crowd-sourcing platforms is a major concern. Incomplete or inaccurate user-generated data prevent truly comprehensive analysis and might lead to inaccurate reports and forecasts. In this paper, we address the problem of assessing the authors of users generated published books in digital libraries. We propose to model the platform using an heterogeneous graph representation and to exploit both the users’ interests and the natural inter-users affinities to infer the authors of unlabelled books. We formalize the task as an optimization problem and integrate in the objective a prior of consistency associated to the networked users in order to capture the neighboors’ interests. Experiments conducted over the Babellio platform (http://babelio.com/), a French crowd-sourcing website for book lovers, achieved successful results and confirm the interest of considering an affinity-based