Crowdsourcing is widely used to solve creative problems of ideation in HCI domain. To improve the creativity of crowdsourcing outcomes, researchers have proposed multiple approaches to support crowd workers’ idea proposal step. Other steps in designers’ creative process, such as information gathering and reflective activity, also impact idea creativity, while their effects in crowdsourcing scenarios remain unexplored. Therefore, referring to the creativity research, this study proposed an approach that optimized crowdsourcing tasks to help crowd workers perform information gathering and reflective activity. Two experiments involving 427 workers were conducted to test the effects of our approach. Results showed that instructing crowd workers to gather information and reflect on their ideas positively affected the creativity of crowdsourcing outcomes. This study provides inspiration for the optimization of crowdsourcing tasks and offers insights to the researchers who focus on using crowd power to achieve the ideation process.

Product presentation video (PPV) is a genre of short-form video in online retailing that presents product features and facilitates online shopping. To improve PPVs, quality and provide a better experience for viewers, PPV producers, most of whom are online retailers and non-professionals in video production, have made efforts to enrich these short-form videos. Despite the great demand for PPV enrichment, these methods have not been systematically explored, and their impacts on user experience remain unclear, impeding the improvement of PPVs. This study combined qualitative and quantitative methods to explore the impacts of PPV enrichment methods on user experience. As an exploratory study, we focused on PPVs of female fashion products on Chinese e-commerce platforms. We collected 240 PPVs and summarized ten enrichment methods from them accordingly. A questionnaire-based experiment, including 48 participants, was then conducted to explore the impacts of these methods on the experience of PPVs. Results indicated that eight out of ten methods effectively improved PPV experience from multiple dimensions. This study brings insights for exploring PPV enrichment from the perspective of user experience and provides support for PPV production process.

Short video is an increasingly prevalent medium in online shopping environments to present products. To cope with the great demand for short videos rising from the enormous number and the rapid update of online products, computer-supported video production is becoming a trend. The optimization of short videos considering user experience is essential. Currently, using text and voice to integrate extra information into short videos is a potential and promising approach for optimizing computer-supported video production, while the effects of these elements on user experience remain unclear. In this study, we conducted a questionnaire-based experiment including 580 participants to explore the impacts of presenting extra information in short videos via text and voice on multi-dimensional user experience. Results indicated that these two elements positively impacted user experience from different dimensions. Gender differences were also found in this study. Based on experimental results, we provided suggestions to support the use of text and voice elements in short video production considering user experience.

Artificial intelligence (AI) has played a significant role in imitating and producing large-scale designs such as e-commerce banners. However, it is less successful at creative and collaborative design outputs. Most humans express their ideas as rough sketches, and lack the professional skills to complete pleasing paintings. Existing AI approaches have failed to convert varied user sketches into artistically beautiful paintings while preserving their semantic concepts. To bridge this gap, we have developed SmartPaint, a co-creative drawing system based on generative adversarial networks (GANs), enabling a machine and a human being to collaborate in cartoon landscape painting. SmartPaint trains a GAN using triples of cartoon images, their corresponding semantic label maps, and edge detection maps. The machine can then simultaneously understand the cartoon style and semantics, along with the spatial relationships among the objects in the landscape images. The trained system receives a sketch as a semantic label map input, and automatically synthesizes its edge map for stable handling of varied sketches. It then outputs a creative and fine painting with the appropriate style corresponding to the human’s sketch. Experiments confirmed that the proposed SmartPaint system successfully generates high-quality cartoon paintings.