No abstract

Lately, crowdsourcing has emerged as a viable option for getting work done by leveraging the collective intelligence of the crowd. With many tasks posted every day, the size of crowdsourcing platforms is growing exponentially. Hence, workers face an important challenge in selecting the right task. Despite the task filtering criteria available on the platform to select the right task, crowd workers find it difficult to choose the most relevant task and must glean through the filtered tasks to find the relevant tasks. In this paper, we propose a framework for recommending tasks to workers. The proposed framework evaluates the worker's fitment over the tasks based on the worker's preference, past tasks he/she has performed, and tasks done by similar workers. We also proposed an approach to estimate the right price for a crowdsourced task for a specific worker. We evaluated our approach on the datasets collected from popular crowdsourcing platforms. Our experimental results show that the recommendation made by our framework for task and price is significantly better as compared with the baseline approach.

No abstract

Crowdsourcing is a process wherein an individual or an organization utilizes the talent pool present over the Internet to accomplish their task.e existing crowdsourcing platforms and their reputation computation are centralized and hence prone to various a acks or malicious manipulation of the data by the central entity. A few distributed crowdsourcing platforms have been proposed but they lack a robust reputation mechanism. So we propose a decentralized crowdsourcing platform having an immutable reputation mechanism to tackle these problems. It is built on top of Ethereum network and does not require the user to trust a third party for a non-malicious experience. It also utilizes IOTA's consensus mechanism which reduces the cost for task evaluation signi cantly.