Worker-Job Recommendation for Mixed Crowdsourcing Systems: Algorithms, Models, Metrics and Service-Oriented Architecture

Crowdsourcing is used as model to distribute work over the Internet via an open call to anonymous human workers, who opt to take up work offerings sometimes for some small compensation. Increasingly, crowdsourcing systems are integrated into workflows to provide human computation capabilities. These workflows consist of machine-based workers that work harmoniously on different phases of a task with their human counterparts. This body of work addresses workflows where machines and human workers have the capacity to fulfill the requirements for same tasks. To maximize performance through the delegation of work to the most competent worker, this work outlines a collaborative filtering based approach with a bottom up evaluation based on workers' performance history and their inferred skillsets. Within the model, there are several algorithms, formulae and evaluative metrics. The work also introduces the notion of an Open Push-Pull model; a paradigm that maximizes on the services and strengths of the open call model, while seeking to address its weaknesses such as platform lock-in that affects access to jobs and availability of the worker pool. The work outlines the model in terms of a service-oriented architecture (SOA). It provides a supporting conceptual model for the architecture and an operational model that facilitates both human and machine workers. It also defines evaluative metrics for understanding the true capabilities of the worker pool. Techniques presented in this work can be used to expand the potential worker pool to compete for tasks through the incorporation of machine-oriented workers via virtualization and other electronic services, and human workers via existing crowds. Results in this work articulate the flexibility of our approach to support both human and machine workers within a competitive model while supporting tasks spanning multiple domains and problem spaces. It addresses the inefficiencies of current top-down approaches in worker-job recommendation through use of a bottom-up approach which adapts to dynamic and rapidly changing data. The work contrasts the shortcomings of top-down approaches' dependency on professed profiles which can be under-represented, over-represented or falsified in other ways with evaluative metrics that can be used for the individual and collective assessment of workers within a labor pool.Ph.D., Computer Science -- Drexel University, 201

Identification of Personality Traits for Recruitment of Unskilled Occupations using Kansei Engineering Method

Job recruitment portals become the main recruitment channel in most of the organizations nowadays because they offer many advantages to recruiters and job applicants. An outstanding recruitment system should be able to filter and recommend the best potential candidates for a job vacancy so that it can avoid hiring of inappropriate individuals or miss out the good candidates. Nevertheless, most of the existing job portals do not cover the unskilled job sectors. Matching unskilled jobs to applicants is challenging because the selection criteria can be very subjective and difficult to specify in terms of professional qualifications. In this paper, Kansei Engineering (KE) Model is applied to find the most prominent personality traits that are preferred by employers in different unskilled job categories in Malaysia. We have identified most prominent 20 Kansei words related to personality traits that are important to six main industries of unskilled workers. The six unskilled sectors involved are construction, hotel, manufacturing, restaurant, sales, and service. 60 employers from the six sectors were interviewed to rank the 50 personality traits identified. Those ranked personality traits can potentially be used for recruitment selection and filtering of unskilled job applicants

JobIQ : recommending study pathways based on career choices

Modern job markets often require an intricate combination of multi-disciplinary skills or specialist and technical knowledge, even for entry-level positions. Such requirements pose increased pressure on higher education graduates entering the job market. This paper presents our JobIQ recommendation system helping prospective students choose educational programs or electives based on their career preferences. While existing recommendation solutions focus on internal institutional data, such as previous student experiences, JobIQ considers external data, recommending educational programs that best cover the knowledge and skills required by selected job roles. To deliver such recommendations, we create and compare skill profiles from job advertisements and educational subjects, aggregating them to skill profiles of job roles and educational programs. Using skill profiles, we build formal models and algorithms for program recommendations. Finally, we suggest other recommendations and benchmarking approaches, helping curriculum developers assess the job readiness of program graduates. The video presenting the JobIQ system is available online∗