Demo Data from SIGIR 2026 / reviewers in the wild / expert
Chunze Xu
dblp:403/4590
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2025
0009-0007-9109-0134ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 2 · 1 first-author · 2 since 2021
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 |
Human-AI interaction · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Recommender systems · 100% | |
| Network and information security
1 paper |
Usable security · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Recommender systems › interactive recommendation
conversational recommendation |
0.3 | 1 | 2025 | Enhancing user information disclosure intention in dynamic conversations of intelligent recommendation systems based on large language models: A perspective of user gratification and privacy calculus · Int. J. Hum. Comput. Stud. 2025 |
Usable security › usable privacy › privacy decision-making
privacy calculus |
0.3 | 1 | 2025 | Enhancing user information disclosure intention in dynamic conversations of intelligent recommendation systems based on large language models: A perspective of user gratification and privacy calculus · Int. J. Hum. Comput. Stud. 2025 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | The Effect of Danmaku Font Size on Online Learning Outcomes for Learners with Different Cognitive Styles: Evidence from Eye MovementsabstractDanmaku is increasingly used during online interactions - such as learner communication in online learning. As an emerging online interaction method, Danmaku not only enhances the viewer interaction experience, but also allows learners to achieve better learning outcomes. Therefore, this study used a 2 (Danmaku font size: large, small) × 2 (cognitive style: field-independent, field-dependent) between-participants design to investigate the effect of Danmaku font size on online learning through eye-tracking studies and to reveal the mechanism of the effect of Danmaku font size on the learning outcomes of field-independent and field-dependent learners. The results show that Danmaku font size affects the learning perception of learners with different cognitive styles in online learning, i.e., under the field-independent cognitive style, small font Danmaku can improve the online learning perceptual experience. Under the field-dependent cognitive style, learners focus more on the Danmaku area, so the Danmaku with large font size can improve the online learning perceptual experience. In summary, this study found that the Danmaku font size impacts the online learning effect of learners with different cognitive styles. Therefore, different Danmaku font sizes should be selected according to different cognitive styles during online teaching. This study can provide research basis and optimization suggestions for how to use Danmaku in online teaching design, which will help deepen the understanding of the role of Danmaku in online teaching. Fengqiang Gao, Chunze Xu |
Int. J. Hum. Comput. Interact. | 2 |
| 2025 | Enhancing user information disclosure intention in dynamic conversations of intelligent recommendation systems based on large language models: A perspective of user gratification and privacy calculus
Chunze Xu, Fengqiang Gao |
Int. J. Hum. Comput. Stud. | 1 |