Real-time recommendation algorithms for crowdsourcing systems

Safran, Mejdl; Che, Dunren

doi:10.1016/j.aci.2016.01.001

Cited by 38 publications

(28 citation statements)

References 14 publications

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“…20 In crowdsourcing contests, the participation history factors that include participation recency and frequency, winning recency and frequency, and tenure and last performance of the competitors are also derived in a study. 21 One of the authors suggests "personalized task recommendation" approach, which aims the matching of the worker interest with the appropriate task and thus makes the 10 Task and participant matching 0.5 0.5 0 0.5 1.5 Nassar and Karray 3 Overview of the crowdsourcing process 0.5 1 0 1 2.5 Javadi Khasraghi and Aghaie 21 Crowdsourcing contests 1 1 0.5 1 3.5 Zheng et al 58 Task design in crowdsourcing 0.5 0.5 0.5 0.5 2 Sales Fonteles et al 59 Trajectory recommendation of tasks 1 0.5 0.5 0.5 2.5 Burnap et al 30 Identifying experts in the crowd for evaluation of engineering designs 0.5 0.5 0.5 0.5 2 Ali-Hassan and Allam 60 Comparing crowdsourcing initiatives toward a typology development 0 0 0 1 1 Wang et al 61 Mobile crowdsourcing framework, challenges, and solutions 0 0 0 1 1 Ghezzi et al 62 Crowdsourcing review 0 0.5 0 1 1.5 Xintong et al 63 Brief survey of crowdsourcing for data mining 0 0 0 1 1 Pournajaf et al 38 Crowd sensing task assignment 0 0 0 1 1 Tarasov et al 64 Worker reliability in crowdsourcing 0 0 0 1 1 Baba et al 39 Improper task detection in crowdsourcing 0.5 0.5 0.5 1 2.5 Geiger and Schader 22 Ptask recommendation in crowdsourcing 0.5 0.5 0 1 2 Hosseini et al 16 Crowdsourcing: A taxonomy and systematic mapping study 0.5 0.5 0 0.5 1.5 Ye and Kankanhalli 65 Organizational task crowdsourcing 0.5 0 0 1 1.5 Ellero et al 66 Real-time crowdsourcing 0 0 0.5 1 1.5 Mao et al 1 Crowdsourcing survey in software engineering 0.5 0.5 0.5 1 2.5 Morschheuser et al 67 Gamified crowdsourcing conceptualization, literature review, and future agenda 0 0 0 1 1 Safran and Che 29 Real-time recommendation algorithms for crowdsourcing systems 1 1 1 1 4 Younas et al 68 Optimal task assignment 0.5 0 1 0.5 2 Harman and Azzam 69 Crowdsourcing criteria and standards 0.5 0 0 0.5 1 Moayedikia et al 31 Task assignment in crowdsourcing platforms 0.5 0 1 0 1.5 Wu et al 70 Task allocation in crowdsourcing 0.5 0.5 1 0 2 Sarı et al…”

Section: What Are the Task Assignment Framework/models Available For Effective Crowdsourcing?mentioning

confidence: 99%

Crowdsourcing usage, task assignment methods, and crowdsourcing platforms: A systematic literature review

ZhenYing

KhanAbdullah

Nazir

et al. 2021

J Software Evolu Process

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Crowdsourcing is simply the outsourcing of different tasks or work to a diverse group of individuals in an open call for the purpose of utilizing human intelligence.Crowdsourcing nowadays used to support and enhance software engineering in different aspects. In this proposed study, a systematic literature review was conducted for the last 10 years from 2010 to 2019. During the filtering process, a total of 120 relevant studies have been identified, and then the most relevant 70 studies were selected to include as part of the current study. The proposed study shows the effect of task assignment in crowdsourcing, such as if a task is assigned to an appropriate worker or an inappropriate worker, what will be the consequences.The study also highlights crowdsourcing usage in the field of software engineering.All the existing task assignment methods used for assigning the task to make crowdsourcing activity more effective have been analyzed. The study also highlights all the available crowdsourcing platforms used for a variety of task to be performed.The study concludes by identifying the issues regarding the task assignment and to specify the methods for enhancement in the assignment of tasks for future research in crowdsourcing.

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Section: What Are the Task Assignment Framework/models Available For Effective Crowdsourcing?mentioning

confidence: 99%

Crowdsourcing usage, task assignment methods, and crowdsourcing platforms: A systematic literature review

ZhenYing

KhanAbdullah

Nazir

et al. 2021

J Software Evolu Process

View full text Add to dashboard Cite

show abstract

“…TOP-K-T and TOP-K-W are the two real-time recommendation algorithms proposed by Safran and Che. e first one computes the appropriate task for a worker and the second one computes the appropriate worker for the task [19]. e expertise prediction heuristics have also been proposed by the authors to identify the experts automatically and to filter a nonexpert 2 Scientific Programming during crowdsourcing activity.…”

Section: Related Workmentioning

confidence: 99%

MCDM Approach for Assigning Task to the Workers by Selected Features Based on Multiple Criteria in Crowdsourcing

Zhao

Khan

et al. 2021

Scientific Programming

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Crowdsourcing in simple words is the outsourcing of a task to an online market to be performed by a diverse group of crowds in order to utilize human intelligence. Due to online labor markets and performing parallel tasks, the crowdsourcing activity is time- and cost-efficient. During crowdsourcing activity, selecting the proper labeled tasks and assigning them to an appropriate worker are a challenge for everyone. A mechanism has been proposed in the current study for assigning the task to the workers. The proposed mechanism is a multicriteria-based task assignment (MBTA) mechanism for assigning the task to the most suitable worker. This mechanism uses approaches for weighting the criteria and ranking the workers. These MCDM methods are Criteria Importance Through Intercriteria Correlation (CRITIC) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Criteria have been made for the workers based on the identified features in the literature. Weight has been assigned to these selected features/criteria with the help of the CRITIC method. The TOPSIS method has been used for the evaluation of workers, with the help of which the ranking of workers is performed in order to get the most suitable worker for the selected tasks to be performed. The proposed work is novel in several ways; for example, the existing methods are mostly based on single criterion or some specific criteria, while this work is based on multiple criteria including all the important features. Furthermore, it is also identified from the literature that none of the authors used MCDM methods for task assignment in crowdsourcing before this research.

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“…Safran Mejdl and Dunren Che [17] proposed a real-time recommendation algorithm for crowdsourcing systems. Real-time recommendations are crying need not for employees but also for the job requesters.…”

Section: Related Workmentioning

confidence: 99%

Development of an Intelligent Job Recommender System for Freelancers using Client’s Feedback Classification and Association Rule Mining Techniques

Hossain¹,

Arefin²

2019

JSW

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Most of the freelancer's time is killed in finding suitable jobs due to the huge number of freelance marketplaces. Freelancing sites send email notifications or show in newsfeed about posted jobs but most of them are irrelevant. Recommending relevant jobs to freelancers to minimize job finding time has drawn the attraction of researchers. Here, in this paper, we propose a recommender system to find out appropriate jobs for freelancers using client's feedback classification and Association rule mining techniques. After collecting the previous work history of freelancers, we analyze the sentiment of client's feedback using Logistic Regression and Linear Support Vector Machine model to classify the completed jobs into two categories: positive and negative. We apply the Association rule mining technique to find out freelancer's frequent skillsets used in both categories of completed jobs. Then, we find out the jobs matched with the positive frequent skillsets using set operations. We also discard jobs that contain negative frequent skillsets. Finally, a collaborative filtering algorithm is applied considering the client's overall rating, the minimum budget/ hourly rate, deadline, re-hire, etc. to generate a more accurate recommendation. After extensive experiments on the real dataset collected from different online marketplaces, we are able to prove that our proposed method correctly recommends the appropriate jobs with 83.40% (Logistic Regression) and 84.03% (Linear SVM) accuracy.

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Real-time recommendation algorithms for crowdsourcing systems

Cited by 38 publications

References 14 publications

Crowdsourcing usage, task assignment methods, and crowdsourcing platforms: A systematic literature review

Crowdsourcing usage, task assignment methods, and crowdsourcing platforms: A systematic literature review

MCDM Approach for Assigning Task to the Workers by Selected Features Based on Multiple Criteria in Crowdsourcing

Development of an Intelligent Job Recommender System for Freelancers using Client’s Feedback Classification and Association Rule Mining Techniques

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