Crowdsourcing worker performance prediction method based on deep knowledge tracking

A technology of knowledge and workers, applied in the field of forecasting, can solve problems that cannot be used directly

Active Publication Date: 2020-03-24
BEIHANG UNIV
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Problems solved by technology

[0006] The present invention aims at the estimation of workers' ability in knowledge-intensive crowdsourcing tasks, and aims to propose a crowdsourcing worker performance prediction method based on deep knowledge tracking to solve the problem that the deep knowledge tracking method cannot be directly used in worker performance prediction

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  • Crowdsourcing worker performance prediction method based on deep knowledge tracking
  • Crowdsourcing worker performance prediction method based on deep knowledge tracking

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Embodiment Construction

[0010] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the following further describes the present invention in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0011] Such as figure 1 As shown, a crowdsourced worker performance prediction method based on deep knowledge tracking proposed by the present invention includes the following steps. Step 1, data preprocessing, transforms the task data form of knowledge-intensive crowdsourced tasks into usable For training the data of the knowledge tracking model, the conversion process includes task result dualizatio...

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Abstract

The invention provides a crowdsourcing worker performance prediction method based on deep knowledge tracking. The crowdsourcing worker performance prediction method comprises the following steps: 1, data preprocessing: converting a task data form of a knowledge-intensive crowdsourcing task into data which can be used for training a knowledge tracking model, and specifically dividing the data intotask result binarization and data splitting; 2, model training: training a deep knowledge tracking model by using the converted data to obtain a worker performance prediction model; and 3, predictingthe performance of the workers, namely predicting the performance of the workers by using the trained model.

Description

Technical field [0001] The invention relates to a prediction method, in particular to a crowdsourced worker performance prediction method based on deep knowledge tracking. Background technique [0002] Knowledge-intensive crowdsourcing is an emerging form of crowdsourcing. The form of knowledge-intensive crowdsourcing is similar to traditional crowdsourcing. Its main form is to publish specific tasks on the Internet platform. Workers accept the tasks and complete them to get paid. A task is often completed by multiple people, and finally released by the platform or task. The person selects the best result through a specific method. Different from traditional crowdsourcing, knowledge-intensive crowdsourcing focuses on more complex tasks, rather than the microtasks in traditional crowdsourcing. Micro-tasks are simple image annotations, common-sense questions and answers, and other tasks that can be completed without professional knowledge and skills. However, knowledge-intensive...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N5/00G06Q10/06
CPCG06N5/00G06Q10/06398
Inventor 孙海龙刘旭东王子哲
Owner BEIHANG UNIV
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