关键词: 竞赛众包, 创意筛选, 关系视角, 语义网络

Abstract:

In crowdsourcing contests, a big challenge is to screen good ideas from a large number of alternative ideas. To address this challenge, this study develops a relation-based intelligent method for idea screening (Rela-IdeaS). This method adopts the strategies of retaining ideas with high quality and eliminating ideas with low quality based on idea novelty and feasibility. Specifically, this study employed the latent dirichlet allocation method to classify the ideas into various themes based on their novelty. Furthermore, the Jaccard coefficient method was used to construct semantic networks of each idea theme and its included ideas. With the help of cumulative distribution functions, these semantic networks were transformed into cumulative distributions Moreover, we used Kolmogorov-Smirnov statistics to measure the distance between semantic networks of ideas and idea themes. Finally, the ideas in each theme were ranked for further screening based on their values of feasibility (i.e., network distance). We applied our method to screen ideas from three crowdsourcing contests. The results indicated that using the proposed method to screen the ideas with low quality has a good precision rate. For screening the ideas with high quality, the method has a good recall rate. Additionally, compared to the commonly used methods, i.e., methods based on idea text length, crowd voting, and the SBERT model, our method performed better in precision and recall rates.

Key words: competition crowdsourcing, idea screening, relation-based perspective, semantic network