Crowdsourcing task data recommendation method based on decision model and genetic matrix decomposition method

Abstract

The invention discloses a crowdsourcing task data recommendation method based on a decision model and a genetic matrix decomposition method. Comprising the following steps: 1) preprocessing historical data in a crowdsourcing platform to obtain preprocessed historical data, and establishing a task feature matrix and a user feature matrix based on the preprocessed historical data; 2) performing information matching on the task feature matrix and the user feature matrix by using a user knowledge fusion decision method to obtain an initial capability matching matrix; 3) establishing a matching decision model according to the task feature matrix and the user feature matrix; and 4) according to the initial capability matching matrix, solving the matching decision model by using a genetic matrix decomposition algorithm to obtain the matching degree between the user and the plurality of tasks, and performing crowdsourcing task recommendation for the user by the crowdsourcing platform based on the matching degree between the user and the plurality of tasks. According to the invention, the precision and efficiency of task recommendation on a crowdsourcing platform are improved, and the realistic problems of task overload and low task matching efficiency in a crowdsourcing scene are effectively solved.

Description

technical field [0001] The invention relates to a crowdsourcing task recommendation method, in particular to a crowdsourcing task data recommendation method based on a decision model and a genetic matrix decomposition method. Background technique [0002] The development of the sharing economy has promoted the rapid development of the crowdsourcing model. The number and types of tasks on the crowdsourcing design platform have increased sharply, and the problem of information overload has emerged. Faced with a lot of information, it is difficult for users to quickly choose the task that suits them. In order to reduce the search cost, users mostly choose tasks that are recently published or ranked in the top two pages. Higher search costs may reduce user engagement and are not conducive to ensuring the quality of task completion. Helping users select tasks related to themselves is the focus of task selection research. Two mainstream task selection methods are task search and ...

AI Technical Summary

Problems solved by technology

Faced with a lot of information, it is difficult for users to quickly select a task that suits them

In order to reduce the search cost, users mostly choose tasks that have been released recently or ranked in the first two pages. Higher search costs may reduce user participation and are not conducive to ensuring the quality of task completion.

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