An online approach for joint task assignment and worker evaluation in crowd-sourcing

The paper tackles the problem of finding the correct solution to a set of binary choice questions or labeling tasks, by adaptively assigning them to workers in a crowdsourcing system. Such problem becomes quite challenging when we do not initially know neither workers' abilities, nor questions' difficulties (besides common a priori statistics), nor (of course) which is the correct answer. Indeed, such problem requires to jointly learn workers' abilities and questions' difficulties, while adaptively assigning questions to the most appropriate workers so as to maximize our chances to find which are the correct answers. To address such problem, we first cast it into a suitably constructed Bayesian framework which permits us to obtain an analytically tractable (closed form) single-question inference step, and then we address the more general framework via the Expectation Propagation algorithm, an approximated message-passing iterative technique. We then exploit the information gathered by the inference framework as adaptive weights for a maximum weight matching task assignment policy, proposing a computationally efficient algorithm which maximizes the entropy reduction for the questions assigned at each step

An online approach for joint task assignment and worker evaluation in crowd-sourcing

The paper tackles the problem of finding the correct solution to a set of multiple choice questions or labeling tasks, by adaptively assigning them to workers in a crowdsourcing system. When we do not initially know anything (besides common a priori statistics) about the workers and the questions involved, such problem becomes quite challenging and requires to jointly learn workers’ abilities and questions’ difficulties, while adaptively assigning questions to the most appropriate workers so as to maximize our chances to find which are the correct answers. To address such problem, we first cast it into a suitably constructed Bayesian framework which permits us to obtain an analytically tractable (closed form) single-question inference step, and then we address the more general framework via the Expectation Propagation algorithm, an approximated message-passing iterative technique. We then exploit the (time-varying) information gathered by the inference framework as adaptive weights for a maximum weight matching task assignment policy, proposing a computationally efficient algorithm which maximizes the entropy reduction for the questions assigned at each step. Experimental results both on synthetic and real-world data shows that the proposed algorithm can significantly improve accuracy in predicting the correct solution to multiple choice questions

Pursuing fit: a grounded theory of e-recruitment in Namibia – an integrated jobseeker and agency perspective

The purpose of this study was to identify the main concern of jobseekers and recruitment agencies in electronic recruitment (e-recruitment) and determine how it was resolved. The country of Namibia was chosen for the study because many of its jobseekers and recruitment agencies are adopting e-recruitment to overcome challenges in their recruitment context. In order to meet the purpose of the study, Classic Grounded Theory Methodology (classic-GTM) was used. Through the application of classic-GTM it emerged that jobseekers' and recruitment agencies' perspectives on e-recruitment are varied and shifting, which together with the dynamics in information technology bring many possibilities and fluidity of stakeholders' behaviour. Therefore, jobseekers and recruitment agencies are mainly concerned about Fit or lack thereof between their conceptualizations of Objects of Concern (namely information technology, jobseekers, job providers (recruitment agencies and employers) and jobs) in such a dynamic environment. Pursuing Fit emerged as the core variable (core category) representing how the participants continuously resolved their main concern. Two sub-categories constituting Pursuing Fit are Interpreting Fit and Positioning for Fit and they explain how stakeholders interpret e-recruitment concepts and position themselves and other Objects of Concern based on interpretation. Recruitment is likely to take place when Objects of Concern relate in a desirable (fitting) manner. The study's contribution to knowledge is through the theory of Pursuing Fit that suggests a systematic way of understanding e-recruitment and of conceptualizing information technology in e-recruitment to increase chances of recruitment. Implications common for both jobseekers and recruitment agencies are context awareness and flexibility. Context awareness allows stakeholders to interpret Objects of Concern based on the context and flexibility makes it possible to change from a previously held position. The study can be used as the foundation for research involving multiple stakeholders in e-recruitment. In conclusion, e-recruitment is a process of meaning creation in which stakeholders interpret concepts and based on the meanings relate the concepts with each other