Demo Data from SIGIR 2026 / reviewers in the wild / expert
Shangyin Zou
dblp:262/4561
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 first-author · 1 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.
| Computer graphics and multimedia
1 paper |
Virtual and augmented reality · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
Haptics and multimodal interaction · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Virtual and augmented reality
multisensory experience |
0.6 | 1 | 2022 | Simulating Olfactory Cocktail Party Effect in VR: A Multi-odor Display Approach Based on Attention · VR 2022 |
Virtual and augmented reality › display
olfactory display |
0.6 | 1 | 2022 | Simulating Olfactory Cocktail Party Effect in VR: A Multi-odor Display Approach Based on Attention · VR 2022 |
Virtual and augmented reality
virtual reality |
0.6 | 1 | 2022 | Simulating Olfactory Cocktail Party Effect in VR: A Multi-odor Display Approach Based on Attention · VR 2022 |
Haptics and multimodal interaction
multimodal interaction |
0.2 | 1 | 2022 | Simulating Olfactory Cocktail Party Effect in VR: A Multi-odor Display Approach Based on Attention · VR 2022 |
Haptics and multimodal interaction › multimodal interaction
olfactory interaction |
0.2 | 1 | 2022 | Simulating Olfactory Cocktail Party Effect in VR: A Multi-odor Display Approach Based on Attention · VR 2022 |
Methods — techniques the papers use, named apart from their topics
user study · 1.1eye tracking · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Simulating Olfactory Cocktail Party Effect in VR: A Multi-odor Display Approach Based on AttentionabstractOdor display has been a popular approach in virtual reality (VR) to enhance users’ multi-sensory experience. The existing multi-odor presentation methods in VR are mostly based on spatiality of scent sources to produce mixed scents, which will possibly compromise users’ olfactory experience because humans normally have poor ability to analyze distinct odorant components from a mixture. To tackle this problem, we present a VR multi-odor display approach that dynamically changes the intensity combinations of different scent sources in the virtual environment according to the user’s attention, hence simulating a virtual cocktail party effect of smell. We acquire the user’s gaze information as attention from the eye-tracking sensors embedded in the head mounted display (HMD), and increase the display intensity of the scent that the user is focusing on to simulate the cocktail party effect of smell, enabling the user to distinguish their desired scent source. We conducted a user study to validate the perception and experience of 2 ways of intensity settings in response to the user’s attention shift, which were a strong level of focused scent mixed with weak levels of non-focused scents and strong focused scent only. The results showed that both of the two intensity settings were able to improve olfactory experience in VR compared to the non-dynamic odor display method. Meanwhile, only the method of presenting strengthened focused scent while maintaining the weaker mixture of background scents showed significant improvement on simulating the olfactory cocktail party effect by giving the users an enhanced sense of their own olfactory sensitivity. Shangyin Zou, Xianyin Hu, Yuki Ban, Shin'ichi Warisawa |
VR | 1 |