Social contact-oriented crowdsourcing task allocating method and system

Abstract

The invention discloses a social contact-oriented crowdsourcing task allocating method and system. The method comprises the steps of carrying out matching degree estimating algorithm calculations on workers and a crowdsourcing task according to the crowdsourcing task published by a task publisher to obtain a group of workers with the highest crowdsourcing task matching degree; calculating the taskallocating by adopting a greedy algorithm and selecting a group of workers with the largest sum of overall matching degrees from the group of workers with the highest crowdsourcing task matching degree to serve as a final allocation result to ensure that each task has different to-be-assigned workers; and waiting for the workers to receive tasks and completing the crowdsourcing task allocating. The method has the advantages that the reference relationship of information of interests and the like of task recommendation is increased to ensure that the task recommendation is more efficient and more accurate; and the algorithm performance is increased and the quality of crowdsourcing services is increased.

Description

technical field [0001] The invention relates to the technical field of the Internet, in particular to a social crowdsourcing task assignment method and system thereof. Background technique [0002] Crowdsourcing is to use some mechanism to make groups participate in something together to achieve a certain goal. Crowdsourcing is a distributed problem-solving and production model. Crowdsourcing is to use the wisdom of groups to solve problems that are difficult for machines to disseminate to groups of workers through public bidding. In the era of social networks, people can further arrange group activities more efficiently through the Internet and carry out cooperative division of labor. Crowdsourcing services combined with social networks has become a hot issue in the field of crowdsourcing research. [0003] The traditional crowdsourcing system does not analyze the impact of social networks on the quality of task completion, and its matching degree estimation algorithm can...

AI Technical Summary

Problems solved by technology

[0003] The traditional crowdsourcing system does not analyze the impact of social networks on the quality of task completion, and its matching degree estimation algorithm cannot solve the system cold start problem well.

Some existing platforms are not designed according to the characteristics of task requirements, so tasks and personnel cannot be further rationally arranged and optimized effectively, resulting in the characteristics of many existing crowdsourcing platforms not fully adapting to the needs of specific customers

[0004] Traditional crowdsourcing platforms only provide task search functions based on keywords, but cannot provide task search based on workers’ interests and preferences, nor can they make recommendations based on the characteristics of workers, making it difficult to meet the individual needs of workers

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