Abstract
Crowdsourcing is the perfect show of collective intelligence, and the key of finishing perfectly the crowdsourcing task is to allocate the appropriate task to the appropriate worker. Now the most of crowdsourcing platforms select tasks through tasks search, but it is short of individual recommendation of tasks. Tag-semantic task recommendation model based on deep learning is proposed in the paper. In this paper, the similarity of word vectors is computed, and the semantic tags similar matrix database is established based on the Word2vec deep learning. The task recommending model is established based on semantic tags to achieve the individual recommendation of crowdsourcing tasks. Through computing the similarity of tags, the relevance between task and worker is obtained, which improves the robustness of task recommendation. Through conducting comparison experiments on Tianpeng web dataset, the effectiveness and applicability of the proposed model are verified.
Highlights
Deep learning was proposed by Geoffrey Hinton et al in 2006
This paper researches the crowdsourcing tasks recommendation model based on Word2vec semantic tags in order to achieve individual recommendation of crowdsourcing tasks [8]
(2) Research the task recommending model based on semantic tags to achieve the individual recommendation of crowdsourcing tasks
Summary
Deep learning was proposed by Geoffrey Hinton et al in 2006. This method simulates human brain neural network to model and realize multiple level abstraction [1, 2]. Task requester, crowdsourcing platform, and worker make up crowdsourcing system [4]. Individual recommendation research of the task is lesser in crowdsourcing, and task selection is relied on hobbies and expertise. This paper researches the crowdsourcing tasks recommendation model based on Word2vec semantic tags in order to achieve individual recommendation of crowdsourcing tasks [8]. Workers (1) Compute the similarity of word vectors and build the semantic tags similar matrix database based on the Word2vec deep learning. (2) Research the task recommending model based on semantic tags to achieve the individual recommendation of crowdsourcing tasks. This paper computes similarity of tasks and workers based on the semantic tag similar matrix.
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