Crowdsourcing Multi-Objective Recommendation System∗

Abstract

Crowdsourcing is an approach whereby employers call for workers online with different capabilities to process a task for monetary reward. With a vast amount of tasks posted every day, satisfying the workers, employers, and service providers who are the stakeholders of any crowdsourcing system is critical to its success. To achieve this, the system should address three objectives: (1) match the worker with suitable tasks that fit the worker's interests and skills and raise the worker's rewards and rating, (2) give the employer more acceptable solutions with lower cost and time and raise the employer's rating, and (3) raise the rate of accepted tasks, which will raise the aggregated commissions to the service provider and improve the average rating of the registered users (employers and workers) accordingly. For these objectives, we present a mechanism design that is capable of reaching holistic satisfaction using a multi-objective recommendation system. In contrast, all previous crowdsourcing recommendation systems are designed to address one stakeholder who could be either the worker or the employer. Moreover, our unique contribution is to consider each stakeholder to be self serving. Considering selfish behavior from every stakeholder, we provide a more qualified recommendation for each stakeholder.

Publication Title

The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018

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