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Yuechen Zhao

dblp:129/2002 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
User interface design and tools · 44% Collaborative and social computing · 44% Usability and user experience research · 13%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Collaborative and social computing › interpersonal relationships
first impressions		 0.212013
Predicting users' first impressions of website aesthetics with a quantification of perceived visual complexity and colorfulness · CHI 2013
User interface design and tools › web site design
website aesthetics		 0.212013
Predicting users' first impressions of website aesthetics with a quantification of perceived visual complexity and colorfulness · CHI 2013

Methods — techniques the papers use, named apart from their topics

online study · 0.2computational modeling · 0.2
YearPublicationVenuePosition
2025 A data-driven method approach for prediction of coal seam gas content combining feature selection and machine learning
Sheng Su, Songwei Wu, Yuechen Zhao, Longyong Shu
 Eng. Appl. Artif. Intell.4
2013 Predicting users' first impressions of website aesthetics with a quantification of perceived visual complexity and colorfulness
abstract
Users make lasting judgments about a website's appeal within a split second of seeing it for the first time. This first impression is influential enough to later affect their opinions of a site's usability and trustworthiness. In this paper, we demonstrate a means to predict the initial impression of aesthetics based on perceptual models of a website's colorfulness and visual complexity. In an online study, we collected ratings of colorfulness, visual complexity, and visual appeal of a set of 450 websites from 548 volunteers. Based on these data, we developed computational models that accurately measure the perceived visual complexity and colorfulness of website screenshots. In combination with demographic variables such as a user's education level and age, these models explain approximately half of the variance in the ratings of aesthetic appeal given after viewing a website for 500ms only.
Katharina Reinecke, Tom Yeh, Luke Miratrix, Rahmatri Mardiko, Yuechen Zhao, Jenny Liu, Krzysztof Z. Gajos
 CHI5