Recommendation of Crowdsourcing Tasks Based on Word2vec Semantic Tags

Dong, Hongbin; Wang, Yingjie; Cai, Zhipeng; Zhang, Lizong

doi:10.1155/2019/2121850

Cited by 15 publications

(10 citation statements)

References 25 publications

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“…19 The details of some of the papers are given in Table 11. 20 Task recommendation in crowdsourcing systems 0.5 0 1 1 2.5 Mao et al 40 Pricing CSD tasks 0.5 1 1 1 3.5 Stol and Fitzgerald 41 CSD case study 1 0 0.5 0.5 2 Cheung et al 42 Distributed time-sensitive task selection 0.5 0 1 0.5 2 Saremi and Yang 18 Simulation of workers and task completion 0 1 0 1 2 Maalej and Ellmann 7 On the similarity of task contexts 0 1 0 0 1 Roy et al 24 Task assignment optimization 0.5 0 1 1 2.5 Qiu et al 26 Crowdsourcing tasks accuracy through behavior prediction and user selection 0.5 0.5 1 1 3 Alelyani and Yang 19 Software crowdsourcing and developers behavior study 0.5 1 1 1 3.5 Machado et al 4 Task allocation in crowdsourcing 0.5 1 1 0.5 3 Borromeo et al 43 Crowd and task influence on crowdsourced work quality 0.5 0.5 0 1 2 Yang et al 12 Dynamic decision support for crowd workers 1 1 1 0.5 3.5 Han et al 44 Budgeted task scheduling for crowdsourced knowledge acquisition 0.5 0 0 0.5 1 Li et al 45 Crowdsourced data management overview and challenges 1 1 0.5 1 3.5 Cui et al 28 Learning task allocation strategies 1 1 1 1 4 Wang et al 14 Recommending CSD by considering Skill Improvement 1 1 0.5 0.5 3 Sanagavarapu and Reddy 46 Crowdsourcing security-opportunities and challenges 0 0.5 0 1 1.5 Costa and Nascimento 37 In-route task selection in crowdsourcing 0.5 0 1 0.5 2 Jiang et al 47 Crowdsourcing task assignment based on workers quality 0.5 0 1 1 2.5 Shin and Paek 48 Task classification in crowdsourcing 0 0 0 1 1 Simão Filho et al 17 Task allocation in distributed SD 0.5 1 1 0 2.5 Pan et al 34 Recommendation of crowdsourcing tasks based on Word2vec semantic tags 0.5 0 1 1 2.5 Weidema et al 49 Toward microtask crowdsourcing software design work 0 0 0 1 1 Song et al 50 Task selection and allocation framework 1 0 1 0 2 Li et al 23 Crowdsourced data management: a survey 1 1 0 1 3 Zhang and Sugiyama 25 Task selection in crowdsourcing 1 1 1 0.5 3.5 Zhang et al 51 On reliable task as...…”

Section: What Is the Role Of Crowdsourcing In Software Engineering?mentioning

confidence: 99%

“…Pan et al 34 Crowdsourcing tasks recommendations Tag semantic task recommendation model is proposed which is based on deep learning. The word vectors similarity is computed and semantic tags matrix data base is established based upon the word2vec deep learning.…”

Section: Cui Et Al Learning Complex Crowdsourcing Task Allocation Strategies From Humansmentioning

confidence: 99%

“…Thus, by similarity computing among tags the relevancy between the task and crowd is identified. 34 T A B L E 1 2 (Continued)…”

Section: Cui Et Al Learning Complex Crowdsourcing Task Allocation Strategies From Humansmentioning

confidence: 99%

See 2 more Smart Citations

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 Is the Role Of Crowdsourcing In Software Engineering?mentioning

confidence: 99%

Section: Cui Et Al Learning Complex Crowdsourcing Task Allocation Strategies From Humansmentioning

confidence: 99%

See 1 more Smart Citation

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

“…en the recommendation model for task is established based upon the Semantic Tags to achieve the recommendation of the tasks in crowdsourcing. e task and worker relevancy is obtained by computing the tags similarity [25]. e Dynamic Utility Task Allocation (DUTA) algorithm was proposed in the paper.…”

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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“…As a result, they have overlooked the semantic and contextual information of keywords, potentially leading to the incorrect categorization of research publications. In this study, one of the most well-known techniques, word embedding, is used 16 – 18 . It can recognize the context of words in a document, such as semantic similarity, grammatical similarity, and relationships with other words.…”

Section: Introductionmentioning

confidence: 99%

Multi-label classification of research articles using Word2Vec and identification of similarity threshold

Mustafa

Usman

et al. 2021

Sci Rep

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Every year, around 28,100 journals publish 2.5 million research publications. Search engines, digital libraries, and citation indexes are used extensively to search these publications. When a user submits a query, it generates a large number of documents among which just a few are relevant. Due to inadequate indexing, the resultant documents are largely unstructured. Publicly known systems mostly index the research papers using keywords rather than using subject hierarchy. Numerous methods reported for performing single-label classification (SLC) or multi-label classification (MLC) are based on content and metadata features. Content-based techniques offer higher outcomes due to the extreme richness of features. But the drawback of content-based techniques is the unavailability of full text in most cases. The use of metadata-based parameters, such as title, keywords, and general terms, acts as an alternative to content. However, existing metadata-based techniques indicate low accuracy due to the use of traditional statistical measures to express textual properties in quantitative form, such as BOW, TF, and TFIDF. These measures may not establish the semantic context of the words. The existing MLC techniques require a specified threshold value to map articles into predetermined categories for which domain knowledge is necessary. The objective of this paper is to get over the limitations of SLC and MLC techniques. To capture the semantic and contextual information of words, the suggested approach leverages the Word2Vec paradigm for textual representation. The suggested model determines threshold values using rigorous data analysis, obviating the necessity for domain expertise. Experimentation is carried out on two datasets from the field of computer science (JUCS and ACM). In comparison to current state-of-the-art methodologies, the proposed model performed well. Experiments yielded average accuracy of 0.86 and 0.84 for JUCS and ACM for SLC, and 0.81 and 0.80 for JUCS and ACM for MLC. On both datasets, the proposed SLC model improved the accuracy up to 4%, while the proposed MLC model increased the accuracy up to 3%.

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Recommendation of Crowdsourcing Tasks Based on Word2vec Semantic Tags

Cited by 15 publications

References 25 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

Multi-label classification of research articles using Word2Vec and identification of similarity threshold

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