The dominant factor of social tags for users’ decision behavior on e‐commerce websites: Color or text

Chen, Xu; Zhang, Qin

doi:10.1002/asi.24118

Cited by 14 publications

(9 citation statements)

References 65 publications

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“…Barton et al 68 found that brightness has a significant effect on sensitivity, and high brightness areas can attract more attention. When red is used as a disturbing factor, the target's attention will be distracted 38 . However, when red is the target factor, its attention capture is magnified 69 …”

Section: Discussionmentioning

confidence: 99%

Experimental study on the relationship between the harmony and cognitive load of business intelligence dashboard color combinations

Tan

Liu

2021

Color Research & Application

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The color combination is an important factor affecting dashboard visual design and is key to triggering the operator's visual harmony and emotion. However, in the actual design process, the relationship among the different color schemes of a dashboard and the operator's harmony, pleasure, and cognitive load has not been effectively analyzed. To solve this problem, two methods, questionnaire measurement and eye movement tracking, were used to evaluate the effects of 24‐color combinations under four‐color schemes of a dashboard by analyzing the changes in color harmony, pleasure, eye movement indicators. The four schemes were red–yellow–blue, green–purple–orange, blue green–blue purple–yellow green, and red orange–yellow orange–red purple. The research results show that the degrees of color harmony and pleasure show a positive correlation. Cognitive load is not affected by color harmony. The larger the amount of information, the higher the cognitive load. This research can be used as a reference when designing and optimizing the color scheme of a business intelligence dashboard.

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Section: Discussionmentioning

confidence: 99%

Experimental study on the relationship between the harmony and cognitive load of business intelligence dashboard color combinations

Tan

Liu

2021

Color Research & Application

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“…Another advantage is reflecting the tag content in the original format of the textual tag. In other words, regardless of the visualization method applied, the original word, language and writing format of the tag will not be changed (Xu and Zhang, 2018). The visualization of a tag does not affect the understanding of the original textual tag's meaning but helps to highlight certain tags that need to be specially noted to improve the efficiency of tag reading.…”

Section: Visualized Textual Tcsmentioning

confidence: 99%

Comparative study of graphic-based tag clouds: theory and experimental evaluation for information search

2020

OIR

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PurposeGraphic-based tag clouds aim to visually represent tag content and tag structure, and then to better represent tagged information for later search. However, few studies have clarified the features among varied visualization approaches involved in graphic-based tag clouds and compared them for the purpose of information search.Design/methodology/approachAfter reviewing four kinds of graphic-based tag clouds, an experimental demonstration was conducted in our study to verify how user performs in information search for a general seeking task by using them. Precision ratio, recall ratio, clicks on search and time for search were four variables tested in the experiment. Also, two supplementary tests were respectively carried out to manifest how graphic-based tag clouds contributed to the identification of target tags and tag clusters.FindingsThe experimental results showed that compared to tag content visual tag structure was more important to find related tags from tag clouds for information search. In addition, tag clouds that visually represented the semantic relationships within tags could make user more confident about their search result and carry out a shorter learning process during searching, which signified a tag-based information search path when visual elements were applied.Originality/valueThis research is one of the first to illustrate systematically the graphic-based tag clouds and their impacts on information search. The research findings could suggest on how to build up more effective and interactive tag clouds and make proposition for the design of search user interface by using graphic-based tag clouds.

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“…Moreover, tags have also been exploited to solve other practical problems, such as interest discovery (Li, Guo, & Zhao, 2008; Yang, Song, & Ji, 2015) and friend recommendation (Feng, Zhang, Wang, & Zhang, 2014; Huang, Zhang, Wang, & Hua, 2015). On e‐commerce websites, tags have also been shown to have an impact on user decision‐making behavior (Xu & Zhang, 2019). However, there are some problems in the manual tagging mechanism.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical attention model for personalized tag recommendation

Sun

Zhu

Jiang

et al. 2020

Asso for Info Science & Tech

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With the development of Web-based social networks, many personalized tag recommendation approaches based on multi-information have been proposed. Due to the differences in users' preferences, different users care about different kinds of information. In the meantime, different elements within each kind of information are differentially informative for user tagging behaviors. In this context, how to effectively integrate different elements and different information separately becomes a key part of tag recommendation. However, the existing methods ignore this key part. In order to address this problem, we propose a deep neural network for tag recommendation. Specifically, we model two important attentive aspects with a hierarchical attention model. For different user-item pairs, the bottom layered attention network models the influence of different elements on the features representation of the information while the top layered attention network models the attentive scores of different information. To verify the effectiveness of the proposed method, we conduct extensive experiments on two real-world data sets. The results show that using attention network and different kinds of information can significantly improve the performance of the recommendation model, and verify the effectiveness and superiority of our proposed model.

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The dominant factor of social tags for users’ decision behavior on e‐commerce websites: Color or text

Cited by 14 publications

References 65 publications

Experimental study on the relationship between the harmony and cognitive load of business intelligence dashboard color combinations

Experimental study on the relationship between the harmony and cognitive load of business intelligence dashboard color combinations

Comparative study of graphic-based tag clouds: theory and experimental evaluation for information search

Hierarchical attention model for personalized tag recommendation

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